{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5525bf60",
   "metadata": {},
   "source": [
    "# MobileNet V2 关键创新点与缺点总结\n",
    "\n",
    "[MobileNet V2](https://arxiv.org/abs/1801.04381 ) 是 Google 提出的一种轻量级卷积神经网络架构，专为移动端和嵌入式设备设计。相比第一代 MobileNet，它在保持高效计算的同时显著提升了模型的准确性。\n",
    "\n",
    "---\n",
    "\n",
    "## 关键创新点\n",
    "\n",
    "### 1. **Inverted Residuals（倒残差结构）**\n",
    "\n",
    "- 使用“扩展-压缩”策略：\n",
    "  - 首先通过 `1x1` 卷积升维（扩展通道数）\n",
    "  - 然后使用 `3x3` 深度可分离卷积提取特征\n",
    "  - 最后通过 `1x1` 卷积降维（压缩通道数）\n",
    "- 该结构为两头小中间大,与传统残差模块中“压缩-扩展”的两头大中间小不同，因此称为“倒置”残差。\n",
    "- 这种设计可以在低维空间中保留更多信息，同时保持轻量化。\n",
    "\n",
    "### 2. **Linear Bottleneck（线性瓶颈层）**\n",
    "\n",
    "- 在最后的降维层中使用 **线性激活函数** 而不是 ReLU6。\n",
    "- 避免了非线性激活对特征表达能力的破坏，尤其在低维空间中尤为重要。\n",
    "\n",
    "这种设计基于如下观测: 低维流形通过线性变换升维之后在用ReLU这种非线性变换相较于降维损失更少的信息,这就是为何采用两头大中间小的结构,在使用ReLU6非线性激活函数时升维,且在降维时不采用ReLU6函数.\n",
    "\n",
    "  ![alt text](resources/mobilenetv2_manifold.png \"Title\")\n",
    "\n",
    "  ![alt text](resources/mobilenetv2_inverted_residual_block.png \"Title\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5d1e83",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70544e18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d1e1748",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        RandomResize([256, 296, 384]),  # 随机在三个size中选择一个进行resize\n",
    "        transforms.RandomRotation(10),\n",
    "        transforms.RandomCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "val_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=train_dataset_transforms,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=val_dataset_transforms,\n",
    ")\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7e8af81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 2254858\n",
      "Epoch: 1/100 Train Loss: 1.9526 Accuracy: 0.3451 Time: 9.18680  | Val Loss: 2.5894 Accuracy: 0.3320\n",
      "Epoch: 2/100 Train Loss: 1.6845 Accuracy: 0.4882 Time: 8.83724  | Val Loss: 1.6842 Accuracy: 0.4963\n",
      "Epoch: 3/100 Train Loss: 1.5540 Accuracy: 0.5535 Time: 11.17765  | Val Loss: 1.5913 Accuracy: 0.5743\n",
      "Epoch: 4/100 Train Loss: 1.4629 Accuracy: 0.5971 Time: 13.43177  | Val Loss: 1.3255 Accuracy: 0.6583\n",
      "Epoch: 5/100 Train Loss: 1.3920 Accuracy: 0.6247 Time: 14.36127  | Val Loss: 1.6434 Accuracy: 0.5567\n",
      "Epoch: 6/100 Train Loss: 1.3219 Accuracy: 0.6598 Time: 13.48745  | Val Loss: 1.2349 Accuracy: 0.7108\n",
      "Epoch: 7/100 Train Loss: 1.2794 Accuracy: 0.6782 Time: 13.25659  | Val Loss: 1.1590 Accuracy: 0.7299\n",
      "Epoch: 8/100 Train Loss: 1.2308 Accuracy: 0.7025 Time: 13.23072  | Val Loss: 1.1284 Accuracy: 0.7483\n",
      "Epoch: 9/100 Train Loss: 1.1914 Accuracy: 0.7169 Time: 14.62846  | Val Loss: 1.2165 Accuracy: 0.7279\n",
      "Epoch: 10/100 Train Loss: 1.1652 Accuracy: 0.7275 Time: 14.21428  | Val Loss: 1.0645 Accuracy: 0.7799\n",
      "Epoch: 11/100 Train Loss: 1.1465 Accuracy: 0.7376 Time: 13.52072  | Val Loss: 1.0880 Accuracy: 0.7771\n",
      "Epoch: 12/100 Train Loss: 1.1278 Accuracy: 0.7444 Time: 13.39481  | Val Loss: 1.0737 Accuracy: 0.7715\n",
      "Epoch: 13/100 Train Loss: 1.0995 Accuracy: 0.7535 Time: 13.77725  | Val Loss: 1.1555 Accuracy: 0.7580\n",
      "Epoch: 14/100 Train Loss: 1.0674 Accuracy: 0.7721 Time: 13.77000  | Val Loss: 0.9987 Accuracy: 0.8028\n",
      "Epoch: 15/100 Train Loss: 1.0582 Accuracy: 0.7759 Time: 13.11033  | Val Loss: 1.0158 Accuracy: 0.8064\n",
      "Epoch: 16/100 Train Loss: 1.0373 Accuracy: 0.7803 Time: 13.26552  | Val Loss: 1.0298 Accuracy: 0.7890\n",
      "Epoch: 17/100 Train Loss: 1.0256 Accuracy: 0.7898 Time: 13.31010  | Val Loss: 0.9963 Accuracy: 0.8036\n",
      "Epoch: 18/100 Train Loss: 1.0041 Accuracy: 0.7936 Time: 14.26080  | Val Loss: 0.9718 Accuracy: 0.8110\n",
      "Epoch: 19/100 Train Loss: 0.9903 Accuracy: 0.8005 Time: 13.73525  | Val Loss: 0.9597 Accuracy: 0.8189\n",
      "Epoch: 20/100 Train Loss: 0.9744 Accuracy: 0.8125 Time: 13.13300  | Val Loss: 0.9802 Accuracy: 0.8094\n",
      "Epoch: 21/100 Train Loss: 0.9722 Accuracy: 0.8041 Time: 13.22798  | Val Loss: 0.9058 Accuracy: 0.8413\n",
      "Epoch: 22/100 Train Loss: 0.9622 Accuracy: 0.8155 Time: 13.76057  | Val Loss: 0.8937 Accuracy: 0.8464\n",
      "Epoch: 23/100 Train Loss: 0.9498 Accuracy: 0.8210 Time: 14.09204  | Val Loss: 0.9046 Accuracy: 0.8362\n",
      "Epoch: 24/100 Train Loss: 0.9443 Accuracy: 0.8205 Time: 13.35061  | Val Loss: 0.8794 Accuracy: 0.8510\n",
      "Epoch: 25/100 Train Loss: 0.9357 Accuracy: 0.8213 Time: 13.29160  | Val Loss: 0.8944 Accuracy: 0.8474\n",
      "Epoch: 26/100 Train Loss: 0.9129 Accuracy: 0.8343 Time: 13.23604  | Val Loss: 0.8762 Accuracy: 0.8538\n",
      "Epoch: 27/100 Train Loss: 0.9006 Accuracy: 0.8413 Time: 14.30719  | Val Loss: 0.9126 Accuracy: 0.8369\n",
      "Epoch: 28/100 Train Loss: 0.9005 Accuracy: 0.8373 Time: 14.07660  | Val Loss: 0.8490 Accuracy: 0.8662\n",
      "Epoch: 29/100 Train Loss: 0.9018 Accuracy: 0.8403 Time: 13.39931  | Val Loss: 0.8771 Accuracy: 0.8522\n",
      "Epoch: 30/100 Train Loss: 0.8765 Accuracy: 0.8494 Time: 13.23143  | Val Loss: 0.8650 Accuracy: 0.8527\n",
      "Epoch: 31/100 Train Loss: 0.8653 Accuracy: 0.8514 Time: 13.80010  | Val Loss: 0.8491 Accuracy: 0.8606\n",
      "Epoch: 32/100 Train Loss: 0.8686 Accuracy: 0.8532 Time: 13.92883  | Val Loss: 0.8744 Accuracy: 0.8487\n",
      "Epoch: 33/100 Train Loss: 0.8546 Accuracy: 0.8608 Time: 13.35494  | Val Loss: 0.9068 Accuracy: 0.8397\n",
      "Epoch: 34/100 Train Loss: 0.8488 Accuracy: 0.8589 Time: 13.31682  | Val Loss: 0.8317 Accuracy: 0.8716\n",
      "Epoch: 35/100 Train Loss: 0.8493 Accuracy: 0.8604 Time: 13.39009  | Val Loss: 0.8502 Accuracy: 0.8639\n",
      "Epoch: 36/100 Train Loss: 0.8300 Accuracy: 0.8744 Time: 14.49791  | Val Loss: 0.8448 Accuracy: 0.8645\n",
      "Epoch: 37/100 Train Loss: 0.8193 Accuracy: 0.8725 Time: 13.88478  | Val Loss: 0.8042 Accuracy: 0.8790\n",
      "Epoch: 38/100 Train Loss: 0.8176 Accuracy: 0.8747 Time: 13.24774  | Val Loss: 0.8491 Accuracy: 0.8624\n",
      "Epoch: 39/100 Train Loss: 0.8028 Accuracy: 0.8776 Time: 13.34709  | Val Loss: 0.8159 Accuracy: 0.8754\n",
      "Epoch: 40/100 Train Loss: 0.8105 Accuracy: 0.8759 Time: 14.22436  | Val Loss: 0.8188 Accuracy: 0.8708\n",
      "Epoch: 41/100 Train Loss: 0.7968 Accuracy: 0.8839 Time: 14.55396  | Val Loss: 0.7946 Accuracy: 0.8843\n",
      "Epoch: 42/100 Train Loss: 0.7846 Accuracy: 0.8874 Time: 13.39370  | Val Loss: 0.8157 Accuracy: 0.8729\n",
      "Epoch: 43/100 Train Loss: 0.7789 Accuracy: 0.8920 Time: 13.46355  | Val Loss: 0.7939 Accuracy: 0.8843\n",
      "Epoch: 44/100 Train Loss: 0.7796 Accuracy: 0.8942 Time: 13.56786  | Val Loss: 0.8067 Accuracy: 0.8843\n",
      "Epoch: 45/100 Train Loss: 0.7708 Accuracy: 0.8965 Time: 14.28264  | Val Loss: 0.7774 Accuracy: 0.8935\n",
      "Epoch: 46/100 Train Loss: 0.7652 Accuracy: 0.8954 Time: 13.49433  | Val Loss: 0.7969 Accuracy: 0.8841\n",
      "Epoch: 47/100 Train Loss: 0.7578 Accuracy: 0.9023 Time: 13.43259  | Val Loss: 0.7633 Accuracy: 0.8989\n",
      "Epoch: 48/100 Train Loss: 0.7597 Accuracy: 0.8981 Time: 13.31282  | Val Loss: 0.7974 Accuracy: 0.8818\n",
      "Epoch: 49/100 Train Loss: 0.7540 Accuracy: 0.9052 Time: 14.63713  | Val Loss: 0.7817 Accuracy: 0.8922\n",
      "Epoch: 50/100 Train Loss: 0.7486 Accuracy: 0.9018 Time: 14.11563  | Val Loss: 0.7902 Accuracy: 0.8846\n",
      "Epoch: 51/100 Train Loss: 0.7387 Accuracy: 0.9069 Time: 13.34195  | Val Loss: 0.7739 Accuracy: 0.8940\n",
      "Epoch: 52/100 Train Loss: 0.7352 Accuracy: 0.9079 Time: 13.37299  | Val Loss: 0.7819 Accuracy: 0.8922\n",
      "Epoch: 53/100 Train Loss: 0.7243 Accuracy: 0.9134 Time: 14.62732  | Val Loss: 0.7849 Accuracy: 0.8915\n",
      "Epoch: 54/100 Train Loss: 0.7200 Accuracy: 0.9155 Time: 14.08860  | Val Loss: 0.7746 Accuracy: 0.8912\n",
      "Epoch: 55/100 Train Loss: 0.7160 Accuracy: 0.9180 Time: 13.12583  | Val Loss: 0.7786 Accuracy: 0.8882\n",
      "Epoch: 56/100 Train Loss: 0.7184 Accuracy: 0.9159 Time: 12.94333  | Val Loss: 0.7632 Accuracy: 0.8950\n",
      "Epoch: 57/100 Train Loss: 0.7049 Accuracy: 0.9230 Time: 13.31904  | Val Loss: 0.7539 Accuracy: 0.9032\n",
      "Epoch: 58/100 Train Loss: 0.6983 Accuracy: 0.9261 Time: 13.68619  | Val Loss: 0.7665 Accuracy: 0.8953\n",
      "Epoch: 59/100 Train Loss: 0.6964 Accuracy: 0.9286 Time: 13.12081  | Val Loss: 0.7562 Accuracy: 0.8986\n",
      "Epoch: 60/100 Train Loss: 0.6980 Accuracy: 0.9242 Time: 12.91954  | Val Loss: 0.7543 Accuracy: 0.9014\n",
      "Epoch: 61/100 Train Loss: 0.6917 Accuracy: 0.9281 Time: 12.81989  | Val Loss: 0.7513 Accuracy: 0.9055\n",
      "Epoch: 62/100 Train Loss: 0.6888 Accuracy: 0.9292 Time: 14.16763  | Val Loss: 0.7467 Accuracy: 0.9080\n",
      "Epoch: 63/100 Train Loss: 0.6812 Accuracy: 0.9295 Time: 13.73605  | Val Loss: 0.7498 Accuracy: 0.9029\n",
      "Epoch: 64/100 Train Loss: 0.6757 Accuracy: 0.9338 Time: 13.01792  | Val Loss: 0.7423 Accuracy: 0.9062\n",
      "Epoch: 65/100 Train Loss: 0.6665 Accuracy: 0.9380 Time: 12.89757  | Val Loss: 0.7492 Accuracy: 0.9037\n",
      "Epoch: 66/100 Train Loss: 0.6712 Accuracy: 0.9355 Time: 13.43541  | Val Loss: 0.7469 Accuracy: 0.9034\n",
      "Epoch: 67/100 Train Loss: 0.6648 Accuracy: 0.9382 Time: 13.72788  | Val Loss: 0.7404 Accuracy: 0.9080\n",
      "Epoch: 68/100 Train Loss: 0.6651 Accuracy: 0.9403 Time: 13.18309  | Val Loss: 0.7401 Accuracy: 0.9057\n",
      "Epoch: 69/100 Train Loss: 0.6565 Accuracy: 0.9427 Time: 13.03801  | Val Loss: 0.7405 Accuracy: 0.9070\n",
      "Epoch: 70/100 Train Loss: 0.6493 Accuracy: 0.9456 Time: 12.88425  | Val Loss: 0.7462 Accuracy: 0.9047\n",
      "Epoch: 71/100 Train Loss: 0.6453 Accuracy: 0.9478 Time: 13.99040  | Val Loss: 0.7406 Accuracy: 0.9057\n",
      "Epoch: 72/100 Train Loss: 0.6454 Accuracy: 0.9474 Time: 13.63219  | Val Loss: 0.7386 Accuracy: 0.9088\n",
      "Epoch: 73/100 Train Loss: 0.6412 Accuracy: 0.9495 Time: 13.15437  | Val Loss: 0.7381 Accuracy: 0.9098\n",
      "Epoch: 74/100 Train Loss: 0.6401 Accuracy: 0.9499 Time: 12.82337  | Val Loss: 0.7393 Accuracy: 0.9068\n",
      "Epoch: 75/100 Train Loss: 0.6326 Accuracy: 0.9518 Time: 13.86190  | Val Loss: 0.7367 Accuracy: 0.9078\n",
      "Epoch: 76/100 Train Loss: 0.6393 Accuracy: 0.9512 Time: 13.73528  | Val Loss: 0.7416 Accuracy: 0.9062\n",
      "Epoch: 77/100 Train Loss: 0.6304 Accuracy: 0.9528 Time: 12.79964  | Val Loss: 0.7372 Accuracy: 0.9098\n",
      "Epoch: 78/100 Train Loss: 0.6258 Accuracy: 0.9569 Time: 13.04329  | Val Loss: 0.7344 Accuracy: 0.9103\n",
      "Epoch: 79/100 Train Loss: 0.6281 Accuracy: 0.9559 Time: 13.25602  | Val Loss: 0.7344 Accuracy: 0.9070\n",
      "Epoch: 80/100 Train Loss: 0.6268 Accuracy: 0.9556 Time: 13.51428  | Val Loss: 0.7353 Accuracy: 0.9106\n",
      "Epoch: 81/100 Train Loss: 0.6216 Accuracy: 0.9582 Time: 13.12907  | Val Loss: 0.7384 Accuracy: 0.9080\n",
      "Epoch: 82/100 Train Loss: 0.6203 Accuracy: 0.9609 Time: 12.85031  | Val Loss: 0.7347 Accuracy: 0.9073\n",
      "Epoch: 83/100 Train Loss: 0.6159 Accuracy: 0.9607 Time: 12.71724  | Val Loss: 0.7288 Accuracy: 0.9108\n",
      "Epoch: 84/100 Train Loss: 0.6171 Accuracy: 0.9610 Time: 13.74629  | Val Loss: 0.7310 Accuracy: 0.9121\n",
      "Epoch: 85/100 Train Loss: 0.6147 Accuracy: 0.9607 Time: 10.48485  | Val Loss: 0.7273 Accuracy: 0.9111\n",
      "Epoch: 86/100 Train Loss: 0.6219 Accuracy: 0.9569 Time: 11.76323  | Val Loss: 0.7294 Accuracy: 0.9124\n",
      "Epoch: 87/100 Train Loss: 0.6124 Accuracy: 0.9605 Time: 13.13710  | Val Loss: 0.7275 Accuracy: 0.9131\n",
      "Epoch: 88/100 Train Loss: 0.6088 Accuracy: 0.9626 Time: 13.05288  | Val Loss: 0.7260 Accuracy: 0.9126\n",
      "Epoch: 89/100 Train Loss: 0.6114 Accuracy: 0.9615 Time: 13.18447  | Val Loss: 0.7252 Accuracy: 0.9129\n",
      "Epoch: 90/100 Train Loss: 0.6035 Accuracy: 0.9659 Time: 12.82452  | Val Loss: 0.7259 Accuracy: 0.9131\n",
      "Epoch: 91/100 Train Loss: 0.6100 Accuracy: 0.9624 Time: 12.78440  | Val Loss: 0.7264 Accuracy: 0.9136\n",
      "Epoch: 92/100 Train Loss: 0.6107 Accuracy: 0.9627 Time: 12.79741  | Val Loss: 0.7225 Accuracy: 0.9159\n",
      "Epoch: 93/100 Train Loss: 0.6023 Accuracy: 0.9645 Time: 12.75568  | Val Loss: 0.7246 Accuracy: 0.9118\n",
      "Epoch: 94/100 Train Loss: 0.6094 Accuracy: 0.9613 Time: 13.23014  | Val Loss: 0.7216 Accuracy: 0.9144\n",
      "Epoch: 95/100 Train Loss: 0.6068 Accuracy: 0.9636 Time: 12.87214  | Val Loss: 0.7242 Accuracy: 0.9136\n",
      "Epoch: 96/100 Train Loss: 0.6062 Accuracy: 0.9646 Time: 13.06035  | Val Loss: 0.7211 Accuracy: 0.9154\n",
      "Epoch: 97/100 Train Loss: 0.6077 Accuracy: 0.9629 Time: 12.80729  | Val Loss: 0.7234 Accuracy: 0.9157\n",
      "Epoch: 98/100 Train Loss: 0.6032 Accuracy: 0.9638 Time: 12.69918  | Val Loss: 0.7248 Accuracy: 0.9159\n",
      "Epoch: 99/100 Train Loss: 0.6069 Accuracy: 0.9638 Time: 13.36970  | Val Loss: 0.7242 Accuracy: 0.9131\n",
      "Epoch: 100/100 Train Loss: 0.6042 Accuracy: 0.9674 Time: 12.76275  | Val Loss: 0.7237 Accuracy: 0.9154\n",
      "#Parameter: 2254858 Accuracy: 0.9154140127388535\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.mobilenet_v2 import MobileNetV2\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier,\n",
    "    lr=1e-3,\n",
    "    weight_decay=1e-5,\n",
    "    max_epochs=100,\n",
    ") -> tuple[MobileNetV2, dict[str, list[float]]]:\n",
    "    net = MobileNetV2(num_classes=10, width_multiplier=width_multiplier).to(DEVICE)\n",
    "    print(f\"#Parameter: {count_trainable_parameter(net)}\")\n",
    "    criteria = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, max_epochs, eta_min=lr / 100\n",
    "    )\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return net, training_stats\n",
    "\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(64, train_dataset, val_dataset)\n",
    "\n",
    "net, width_multiplier_1 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier=1,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37f41a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 1292578\n",
      "Epoch: 1/100 Train Loss: 1.9849 Accuracy: 0.3295 Time: 10.69298  | Val Loss: 1.8432 Accuracy: 0.4260\n",
      "Epoch: 2/100 Train Loss: 1.7107 Accuracy: 0.4740 Time: 10.53086  | Val Loss: 2.1749 Accuracy: 0.3906\n",
      "Epoch: 3/100 Train Loss: 1.5737 Accuracy: 0.5389 Time: 10.49241  | Val Loss: 1.5296 Accuracy: 0.5656\n",
      "Epoch: 4/100 Train Loss: 1.4806 Accuracy: 0.5840 Time: 10.49923  | Val Loss: 1.3858 Accuracy: 0.6346\n",
      "Epoch: 5/100 Train Loss: 1.4083 Accuracy: 0.6166 Time: 11.41891  | Val Loss: 1.2976 Accuracy: 0.6680\n",
      "Epoch: 6/100 Train Loss: 1.3293 Accuracy: 0.6529 Time: 11.16928  | Val Loss: 1.3777 Accuracy: 0.6502\n",
      "Epoch: 7/100 Train Loss: 1.3018 Accuracy: 0.6666 Time: 10.58485  | Val Loss: 1.2529 Accuracy: 0.6922\n",
      "Epoch: 8/100 Train Loss: 1.2623 Accuracy: 0.6866 Time: 10.39270  | Val Loss: 1.1974 Accuracy: 0.7126\n",
      "Epoch: 9/100 Train Loss: 1.2245 Accuracy: 0.6963 Time: 11.31945  | Val Loss: 1.1712 Accuracy: 0.7396\n",
      "Epoch: 10/100 Train Loss: 1.1817 Accuracy: 0.7247 Time: 11.08638  | Val Loss: 1.1771 Accuracy: 0.7292\n",
      "Epoch: 11/100 Train Loss: 1.1587 Accuracy: 0.7293 Time: 10.90280  | Val Loss: 1.0776 Accuracy: 0.7748\n",
      "Epoch: 12/100 Train Loss: 1.1349 Accuracy: 0.7458 Time: 10.44878  | Val Loss: 1.1351 Accuracy: 0.7501\n",
      "Epoch: 13/100 Train Loss: 1.1278 Accuracy: 0.7440 Time: 10.36324  | Val Loss: 1.0635 Accuracy: 0.7781\n",
      "Epoch: 14/100 Train Loss: 1.1054 Accuracy: 0.7514 Time: 11.42613  | Val Loss: 1.0575 Accuracy: 0.7768\n",
      "Epoch: 15/100 Train Loss: 1.0867 Accuracy: 0.7597 Time: 11.22946  | Val Loss: 1.1170 Accuracy: 0.7457\n",
      "Epoch: 16/100 Train Loss: 1.0695 Accuracy: 0.7708 Time: 10.56469  | Val Loss: 0.9762 Accuracy: 0.8178\n",
      "Epoch: 17/100 Train Loss: 1.0528 Accuracy: 0.7733 Time: 10.55057  | Val Loss: 1.0198 Accuracy: 0.8013\n",
      "Epoch: 18/100 Train Loss: 1.0236 Accuracy: 0.7829 Time: 10.67419  | Val Loss: 0.9867 Accuracy: 0.8054\n",
      "Epoch: 19/100 Train Loss: 1.0139 Accuracy: 0.7914 Time: 11.44663  | Val Loss: 1.0120 Accuracy: 0.8015\n",
      "Epoch: 20/100 Train Loss: 1.0116 Accuracy: 0.7902 Time: 10.99755  | Val Loss: 0.9351 Accuracy: 0.8318\n",
      "Epoch: 21/100 Train Loss: 0.9853 Accuracy: 0.8069 Time: 10.43435  | Val Loss: 0.9657 Accuracy: 0.8217\n",
      "Epoch: 22/100 Train Loss: 0.9784 Accuracy: 0.8074 Time: 10.29343  | Val Loss: 0.9987 Accuracy: 0.8051\n",
      "Epoch: 23/100 Train Loss: 0.9677 Accuracy: 0.8076 Time: 11.00885  | Val Loss: 1.0072 Accuracy: 0.7913\n",
      "Epoch: 24/100 Train Loss: 0.9661 Accuracy: 0.8151 Time: 11.06944  | Val Loss: 0.9457 Accuracy: 0.8222\n",
      "Epoch: 25/100 Train Loss: 0.9630 Accuracy: 0.8138 Time: 10.96699  | Val Loss: 0.9275 Accuracy: 0.8296\n",
      "Epoch: 26/100 Train Loss: 0.9478 Accuracy: 0.8215 Time: 10.52898  | Val Loss: 0.9075 Accuracy: 0.8385\n",
      "Epoch: 27/100 Train Loss: 0.9289 Accuracy: 0.8236 Time: 10.46614  | Val Loss: 0.9242 Accuracy: 0.8354\n",
      "Epoch: 28/100 Train Loss: 0.9372 Accuracy: 0.8216 Time: 11.47749  | Val Loss: 0.9273 Accuracy: 0.8354\n",
      "Epoch: 29/100 Train Loss: 0.9023 Accuracy: 0.8403 Time: 10.92787  | Val Loss: 0.9022 Accuracy: 0.8441\n",
      "Epoch: 30/100 Train Loss: 0.9108 Accuracy: 0.8336 Time: 10.39624  | Val Loss: 0.9168 Accuracy: 0.8357\n",
      "Epoch: 31/100 Train Loss: 0.8982 Accuracy: 0.8400 Time: 10.31880  | Val Loss: 0.9084 Accuracy: 0.8446\n",
      "Epoch: 32/100 Train Loss: 0.8847 Accuracy: 0.8478 Time: 10.77863  | Val Loss: 0.8587 Accuracy: 0.8637\n",
      "Epoch: 33/100 Train Loss: 0.8853 Accuracy: 0.8450 Time: 11.05406  | Val Loss: 0.8801 Accuracy: 0.8563\n",
      "Epoch: 34/100 Train Loss: 0.8745 Accuracy: 0.8483 Time: 10.92543  | Val Loss: 0.8834 Accuracy: 0.8489\n",
      "Epoch: 35/100 Train Loss: 0.8585 Accuracy: 0.8592 Time: 10.52123  | Val Loss: 0.8819 Accuracy: 0.8566\n",
      "Epoch: 36/100 Train Loss: 0.8638 Accuracy: 0.8531 Time: 10.32734  | Val Loss: 0.8869 Accuracy: 0.8487\n",
      "Epoch: 37/100 Train Loss: 0.8512 Accuracy: 0.8626 Time: 11.25431  | Val Loss: 0.8551 Accuracy: 0.8581\n",
      "Epoch: 38/100 Train Loss: 0.8397 Accuracy: 0.8681 Time: 10.94515  | Val Loss: 0.8736 Accuracy: 0.8515\n",
      "Epoch: 39/100 Train Loss: 0.8371 Accuracy: 0.8639 Time: 10.31568  | Val Loss: 0.8731 Accuracy: 0.8563\n",
      "Epoch: 40/100 Train Loss: 0.8244 Accuracy: 0.8704 Time: 10.37780  | Val Loss: 0.8375 Accuracy: 0.8678\n",
      "Epoch: 41/100 Train Loss: 0.8319 Accuracy: 0.8695 Time: 11.12912  | Val Loss: 0.8700 Accuracy: 0.8614\n",
      "Epoch: 42/100 Train Loss: 0.8358 Accuracy: 0.8663 Time: 10.86517  | Val Loss: 0.8485 Accuracy: 0.8637\n",
      "Epoch: 43/100 Train Loss: 0.8130 Accuracy: 0.8759 Time: 10.60639  | Val Loss: 0.8410 Accuracy: 0.8678\n",
      "Epoch: 44/100 Train Loss: 0.8049 Accuracy: 0.8808 Time: 10.41122  | Val Loss: 0.8680 Accuracy: 0.8548\n",
      "Epoch: 45/100 Train Loss: 0.8111 Accuracy: 0.8786 Time: 10.44025  | Val Loss: 0.8219 Accuracy: 0.8772\n",
      "Epoch: 46/100 Train Loss: 0.7972 Accuracy: 0.8826 Time: 11.14565  | Val Loss: 0.8389 Accuracy: 0.8696\n",
      "Epoch: 47/100 Train Loss: 0.7934 Accuracy: 0.8857 Time: 10.98865  | Val Loss: 0.8711 Accuracy: 0.8520\n",
      "Epoch: 48/100 Train Loss: 0.7920 Accuracy: 0.8870 Time: 10.28440  | Val Loss: 0.8218 Accuracy: 0.8744\n",
      "Epoch: 49/100 Train Loss: 0.7761 Accuracy: 0.8964 Time: 10.32660  | Val Loss: 0.8196 Accuracy: 0.8813\n",
      "Epoch: 50/100 Train Loss: 0.7657 Accuracy: 0.8993 Time: 11.10364  | Val Loss: 0.7947 Accuracy: 0.8859\n",
      "Epoch: 51/100 Train Loss: 0.7607 Accuracy: 0.8985 Time: 11.12644  | Val Loss: 0.8273 Accuracy: 0.8749\n",
      "Epoch: 52/100 Train Loss: 0.7642 Accuracy: 0.8968 Time: 10.80687  | Val Loss: 0.8184 Accuracy: 0.8718\n",
      "Epoch: 53/100 Train Loss: 0.7565 Accuracy: 0.9012 Time: 10.41867  | Val Loss: 0.8031 Accuracy: 0.8808\n",
      "Epoch: 54/100 Train Loss: 0.7468 Accuracy: 0.9042 Time: 10.57003  | Val Loss: 0.8120 Accuracy: 0.8790\n",
      "Epoch: 55/100 Train Loss: 0.7360 Accuracy: 0.9106 Time: 11.20774  | Val Loss: 0.8306 Accuracy: 0.8652\n",
      "Epoch: 56/100 Train Loss: 0.7329 Accuracy: 0.9125 Time: 11.00679  | Val Loss: 0.8209 Accuracy: 0.8782\n",
      "Epoch: 57/100 Train Loss: 0.7357 Accuracy: 0.9101 Time: 10.41901  | Val Loss: 0.8000 Accuracy: 0.8820\n",
      "Epoch: 58/100 Train Loss: 0.7254 Accuracy: 0.9140 Time: 10.55966  | Val Loss: 0.7943 Accuracy: 0.8851\n",
      "Epoch: 59/100 Train Loss: 0.7257 Accuracy: 0.9132 Time: 10.80919  | Val Loss: 0.7969 Accuracy: 0.8864\n",
      "Epoch: 60/100 Train Loss: 0.7118 Accuracy: 0.9230 Time: 11.39664  | Val Loss: 0.8007 Accuracy: 0.8818\n",
      "Epoch: 61/100 Train Loss: 0.7175 Accuracy: 0.9184 Time: 11.50683  | Val Loss: 0.8075 Accuracy: 0.8843\n",
      "Epoch: 62/100 Train Loss: 0.7137 Accuracy: 0.9210 Time: 8.59611  | Val Loss: 0.7892 Accuracy: 0.8876\n",
      "Epoch: 63/100 Train Loss: 0.7098 Accuracy: 0.9254 Time: 8.59788  | Val Loss: 0.7876 Accuracy: 0.8861\n",
      "Epoch: 64/100 Train Loss: 0.7085 Accuracy: 0.9254 Time: 9.52437  | Val Loss: 0.7777 Accuracy: 0.8904\n",
      "Epoch: 65/100 Train Loss: 0.7003 Accuracy: 0.9260 Time: 10.94743  | Val Loss: 0.7871 Accuracy: 0.8882\n",
      "Epoch: 66/100 Train Loss: 0.6872 Accuracy: 0.9354 Time: 10.54559  | Val Loss: 0.7809 Accuracy: 0.8925\n",
      "Epoch: 67/100 Train Loss: 0.6919 Accuracy: 0.9301 Time: 10.68031  | Val Loss: 0.7812 Accuracy: 0.8866\n",
      "Epoch: 68/100 Train Loss: 0.6850 Accuracy: 0.9316 Time: 10.44140  | Val Loss: 0.7744 Accuracy: 0.8904\n",
      "Epoch: 69/100 Train Loss: 0.6780 Accuracy: 0.9359 Time: 10.61629  | Val Loss: 0.7746 Accuracy: 0.8938\n",
      "Epoch: 70/100 Train Loss: 0.6829 Accuracy: 0.9343 Time: 10.73340  | Val Loss: 0.7889 Accuracy: 0.8841\n",
      "Epoch: 71/100 Train Loss: 0.6737 Accuracy: 0.9393 Time: 10.46278  | Val Loss: 0.7738 Accuracy: 0.8950\n",
      "Epoch: 72/100 Train Loss: 0.6702 Accuracy: 0.9423 Time: 10.42510  | Val Loss: 0.7874 Accuracy: 0.8864\n",
      "Epoch: 73/100 Train Loss: 0.6690 Accuracy: 0.9420 Time: 10.53842  | Val Loss: 0.7799 Accuracy: 0.8892\n",
      "Epoch: 74/100 Train Loss: 0.6656 Accuracy: 0.9402 Time: 10.50159  | Val Loss: 0.7786 Accuracy: 0.8889\n",
      "Epoch: 75/100 Train Loss: 0.6707 Accuracy: 0.9377 Time: 10.41107  | Val Loss: 0.7679 Accuracy: 0.8940\n",
      "Epoch: 76/100 Train Loss: 0.6677 Accuracy: 0.9430 Time: 10.72918  | Val Loss: 0.7722 Accuracy: 0.8940\n",
      "Epoch: 77/100 Train Loss: 0.6543 Accuracy: 0.9478 Time: 10.37003  | Val Loss: 0.7768 Accuracy: 0.8917\n",
      "Epoch: 78/100 Train Loss: 0.6631 Accuracy: 0.9435 Time: 10.75877  | Val Loss: 0.7709 Accuracy: 0.8953\n",
      "Epoch: 79/100 Train Loss: 0.6528 Accuracy: 0.9465 Time: 10.55233  | Val Loss: 0.7682 Accuracy: 0.8915\n",
      "Epoch: 80/100 Train Loss: 0.6497 Accuracy: 0.9487 Time: 10.78067  | Val Loss: 0.7666 Accuracy: 0.8963\n",
      "Epoch: 81/100 Train Loss: 0.6470 Accuracy: 0.9500 Time: 10.36914  | Val Loss: 0.7705 Accuracy: 0.8884\n",
      "Epoch: 82/100 Train Loss: 0.6519 Accuracy: 0.9475 Time: 10.80839  | Val Loss: 0.7635 Accuracy: 0.8950\n",
      "Epoch: 83/100 Train Loss: 0.6478 Accuracy: 0.9504 Time: 10.75830  | Val Loss: 0.7646 Accuracy: 0.8950\n",
      "Epoch: 84/100 Train Loss: 0.6486 Accuracy: 0.9493 Time: 10.80901  | Val Loss: 0.7653 Accuracy: 0.8940\n",
      "Epoch: 85/100 Train Loss: 0.6459 Accuracy: 0.9516 Time: 10.45882  | Val Loss: 0.7673 Accuracy: 0.8961\n",
      "Epoch: 86/100 Train Loss: 0.6365 Accuracy: 0.9554 Time: 10.28078  | Val Loss: 0.7633 Accuracy: 0.8945\n",
      "Epoch: 87/100 Train Loss: 0.6361 Accuracy: 0.9558 Time: 10.91375  | Val Loss: 0.7603 Accuracy: 0.8968\n",
      "Epoch: 88/100 Train Loss: 0.6415 Accuracy: 0.9527 Time: 10.70683  | Val Loss: 0.7630 Accuracy: 0.8953\n",
      "Epoch: 89/100 Train Loss: 0.6398 Accuracy: 0.9509 Time: 10.77649  | Val Loss: 0.7629 Accuracy: 0.8950\n",
      "Epoch: 90/100 Train Loss: 0.6357 Accuracy: 0.9553 Time: 10.50631  | Val Loss: 0.7633 Accuracy: 0.8948\n",
      "Epoch: 91/100 Train Loss: 0.6389 Accuracy: 0.9533 Time: 10.76118  | Val Loss: 0.7596 Accuracy: 0.8976\n",
      "Epoch: 92/100 Train Loss: 0.6368 Accuracy: 0.9553 Time: 10.79988  | Val Loss: 0.7588 Accuracy: 0.8978\n",
      "Epoch: 93/100 Train Loss: 0.6347 Accuracy: 0.9549 Time: 10.89444  | Val Loss: 0.7628 Accuracy: 0.8950\n",
      "Epoch: 94/100 Train Loss: 0.6300 Accuracy: 0.9585 Time: 10.58322  | Val Loss: 0.7594 Accuracy: 0.8958\n",
      "Epoch: 95/100 Train Loss: 0.6337 Accuracy: 0.9555 Time: 10.84331  | Val Loss: 0.7595 Accuracy: 0.8950\n",
      "Epoch: 96/100 Train Loss: 0.6317 Accuracy: 0.9573 Time: 10.44766  | Val Loss: 0.7603 Accuracy: 0.8963\n",
      "Epoch: 97/100 Train Loss: 0.6307 Accuracy: 0.9561 Time: 10.65662  | Val Loss: 0.7629 Accuracy: 0.8940\n",
      "Epoch: 98/100 Train Loss: 0.6338 Accuracy: 0.9552 Time: 10.33588  | Val Loss: 0.7578 Accuracy: 0.8983\n",
      "Epoch: 99/100 Train Loss: 0.6254 Accuracy: 0.9606 Time: 10.47426  | Val Loss: 0.7567 Accuracy: 0.8981\n",
      "Epoch: 100/100 Train Loss: 0.6426 Accuracy: 0.9519 Time: 10.77126  | Val Loss: 0.7605 Accuracy: 0.8961\n",
      "#Parameter: 1292578 Accuracy: 0.8960509554140127\n"
     ]
    }
   ],
   "source": [
    "net, width_multiplier_75 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier=0.75,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f324653c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 596010\n",
      "Epoch: 1/100 Train Loss: 2.0828 Accuracy: 0.2460 Time: 8.91988  | Val Loss: 2.0095 Accuracy: 0.3266\n",
      "Epoch: 2/100 Train Loss: 1.8159 Accuracy: 0.4098 Time: 8.75082  | Val Loss: 1.7573 Accuracy: 0.4428\n",
      "Epoch: 3/100 Train Loss: 1.6885 Accuracy: 0.4766 Time: 8.54792  | Val Loss: 1.6885 Accuracy: 0.4741\n",
      "Epoch: 4/100 Train Loss: 1.5973 Accuracy: 0.5267 Time: 8.80671  | Val Loss: 1.5289 Accuracy: 0.5643\n",
      "Epoch: 5/100 Train Loss: 1.5256 Accuracy: 0.5613 Time: 8.86751  | Val Loss: 1.4175 Accuracy: 0.6092\n",
      "Epoch: 6/100 Train Loss: 1.4443 Accuracy: 0.5972 Time: 8.77320  | Val Loss: 1.4652 Accuracy: 0.5952\n",
      "Epoch: 7/100 Train Loss: 1.4129 Accuracy: 0.6117 Time: 8.81523  | Val Loss: 1.3391 Accuracy: 0.6540\n",
      "Epoch: 8/100 Train Loss: 1.3785 Accuracy: 0.6288 Time: 9.31864  | Val Loss: 1.2794 Accuracy: 0.6795\n",
      "Epoch: 9/100 Train Loss: 1.3291 Accuracy: 0.6572 Time: 8.92377  | Val Loss: 1.2585 Accuracy: 0.6884\n",
      "Epoch: 10/100 Train Loss: 1.2984 Accuracy: 0.6622 Time: 8.51917  | Val Loss: 1.2705 Accuracy: 0.6833\n",
      "Epoch: 11/100 Train Loss: 1.2653 Accuracy: 0.6769 Time: 8.71178  | Val Loss: 1.1995 Accuracy: 0.7121\n",
      "Epoch: 12/100 Train Loss: 1.2297 Accuracy: 0.6952 Time: 9.10467  | Val Loss: 1.1753 Accuracy: 0.7271\n",
      "Epoch: 13/100 Train Loss: 1.2015 Accuracy: 0.7064 Time: 9.00126  | Val Loss: 1.1139 Accuracy: 0.7503\n",
      "Epoch: 14/100 Train Loss: 1.1735 Accuracy: 0.7210 Time: 8.66168  | Val Loss: 1.1820 Accuracy: 0.7197\n",
      "Epoch: 15/100 Train Loss: 1.1460 Accuracy: 0.7337 Time: 8.72602  | Val Loss: 1.1325 Accuracy: 0.7429\n",
      "Epoch: 16/100 Train Loss: 1.1425 Accuracy: 0.7344 Time: 9.10256  | Val Loss: 1.1200 Accuracy: 0.7483\n",
      "Epoch: 17/100 Train Loss: 1.1232 Accuracy: 0.7489 Time: 8.86838  | Val Loss: 1.0804 Accuracy: 0.7715\n",
      "Epoch: 18/100 Train Loss: 1.1034 Accuracy: 0.7562 Time: 8.41029  | Val Loss: 1.0401 Accuracy: 0.7766\n",
      "Epoch: 19/100 Train Loss: 1.0753 Accuracy: 0.7692 Time: 8.48053  | Val Loss: 1.0319 Accuracy: 0.7880\n",
      "Epoch: 20/100 Train Loss: 1.0730 Accuracy: 0.7688 Time: 9.00678  | Val Loss: 1.0090 Accuracy: 0.7972\n",
      "Epoch: 21/100 Train Loss: 1.0598 Accuracy: 0.7760 Time: 8.91504  | Val Loss: 1.0427 Accuracy: 0.7880\n",
      "Epoch: 22/100 Train Loss: 1.0484 Accuracy: 0.7758 Time: 8.47267  | Val Loss: 1.0723 Accuracy: 0.7712\n",
      "Epoch: 23/100 Train Loss: 1.0345 Accuracy: 0.7834 Time: 8.34164  | Val Loss: 0.9734 Accuracy: 0.8094\n",
      "Epoch: 24/100 Train Loss: 1.0246 Accuracy: 0.7915 Time: 8.85973  | Val Loss: 1.0000 Accuracy: 0.8023\n",
      "Epoch: 25/100 Train Loss: 1.0059 Accuracy: 0.7952 Time: 8.75458  | Val Loss: 0.9742 Accuracy: 0.8104\n",
      "Epoch: 26/100 Train Loss: 1.0012 Accuracy: 0.7980 Time: 8.41567  | Val Loss: 0.9620 Accuracy: 0.8145\n",
      "Epoch: 27/100 Train Loss: 0.9850 Accuracy: 0.8019 Time: 8.25183  | Val Loss: 0.9858 Accuracy: 0.8183\n",
      "Epoch: 28/100 Train Loss: 0.9872 Accuracy: 0.8069 Time: 8.78310  | Val Loss: 0.9424 Accuracy: 0.8262\n",
      "Epoch: 29/100 Train Loss: 0.9709 Accuracy: 0.8097 Time: 8.80692  | Val Loss: 0.9611 Accuracy: 0.8155\n",
      "Epoch: 30/100 Train Loss: 0.9617 Accuracy: 0.8156 Time: 8.89204  | Val Loss: 0.9740 Accuracy: 0.8122\n",
      "Epoch: 31/100 Train Loss: 0.9616 Accuracy: 0.8160 Time: 8.55195  | Val Loss: 0.9488 Accuracy: 0.8245\n",
      "Epoch: 32/100 Train Loss: 0.9458 Accuracy: 0.8259 Time: 8.61585  | Val Loss: 0.9542 Accuracy: 0.8171\n",
      "Epoch: 33/100 Train Loss: 0.9610 Accuracy: 0.8172 Time: 7.19216  | Val Loss: 0.9098 Accuracy: 0.8354\n",
      "Epoch: 34/100 Train Loss: 0.9284 Accuracy: 0.8290 Time: 6.85719  | Val Loss: 0.9426 Accuracy: 0.8229\n",
      "Epoch: 35/100 Train Loss: 0.9268 Accuracy: 0.8308 Time: 6.93702  | Val Loss: 0.9097 Accuracy: 0.8436\n",
      "Epoch: 36/100 Train Loss: 0.9140 Accuracy: 0.8393 Time: 6.88837  | Val Loss: 0.9205 Accuracy: 0.8364\n",
      "Epoch: 37/100 Train Loss: 0.9134 Accuracy: 0.8353 Time: 8.91150  | Val Loss: 0.9072 Accuracy: 0.8367\n",
      "Epoch: 38/100 Train Loss: 0.9112 Accuracy: 0.8406 Time: 8.54310  | Val Loss: 0.8830 Accuracy: 0.8474\n",
      "Epoch: 39/100 Train Loss: 0.8934 Accuracy: 0.8479 Time: 8.76622  | Val Loss: 0.9284 Accuracy: 0.8375\n",
      "Epoch: 40/100 Train Loss: 0.8900 Accuracy: 0.8450 Time: 8.85309  | Val Loss: 0.8895 Accuracy: 0.8515\n",
      "Epoch: 41/100 Train Loss: 0.8882 Accuracy: 0.8478 Time: 8.98401  | Val Loss: 0.8962 Accuracy: 0.8454\n",
      "Epoch: 42/100 Train Loss: 0.8820 Accuracy: 0.8508 Time: 9.66136  | Val Loss: 0.8717 Accuracy: 0.8555\n",
      "Epoch: 43/100 Train Loss: 0.8713 Accuracy: 0.8558 Time: 9.18385  | Val Loss: 0.8715 Accuracy: 0.8581\n",
      "Epoch: 44/100 Train Loss: 0.8584 Accuracy: 0.8601 Time: 8.79856  | Val Loss: 0.8653 Accuracy: 0.8550\n",
      "Epoch: 45/100 Train Loss: 0.8562 Accuracy: 0.8601 Time: 8.71172  | Val Loss: 0.8710 Accuracy: 0.8563\n",
      "Epoch: 46/100 Train Loss: 0.8473 Accuracy: 0.8642 Time: 8.65065  | Val Loss: 0.9018 Accuracy: 0.8476\n",
      "Epoch: 47/100 Train Loss: 0.8471 Accuracy: 0.8667 Time: 8.50696  | Val Loss: 0.8620 Accuracy: 0.8632\n",
      "Epoch: 48/100 Train Loss: 0.8389 Accuracy: 0.8706 Time: 8.50473  | Val Loss: 0.8513 Accuracy: 0.8688\n",
      "Epoch: 49/100 Train Loss: 0.8393 Accuracy: 0.8701 Time: 8.63009  | Val Loss: 0.8671 Accuracy: 0.8586\n",
      "Epoch: 50/100 Train Loss: 0.8275 Accuracy: 0.8716 Time: 8.85037  | Val Loss: 0.8497 Accuracy: 0.8639\n",
      "Epoch: 51/100 Train Loss: 0.8234 Accuracy: 0.8762 Time: 8.65852  | Val Loss: 0.8659 Accuracy: 0.8578\n",
      "Epoch: 52/100 Train Loss: 0.8176 Accuracy: 0.8768 Time: 9.06151  | Val Loss: 0.8523 Accuracy: 0.8637\n",
      "Epoch: 53/100 Train Loss: 0.8124 Accuracy: 0.8812 Time: 8.94788  | Val Loss: 0.8600 Accuracy: 0.8578\n",
      "Epoch: 54/100 Train Loss: 0.8024 Accuracy: 0.8847 Time: 9.25162  | Val Loss: 0.8506 Accuracy: 0.8614\n",
      "Epoch: 55/100 Train Loss: 0.7933 Accuracy: 0.8905 Time: 9.08514  | Val Loss: 0.8500 Accuracy: 0.8586\n",
      "Epoch: 56/100 Train Loss: 0.7967 Accuracy: 0.8907 Time: 8.89179  | Val Loss: 0.8352 Accuracy: 0.8693\n",
      "Epoch: 57/100 Train Loss: 0.8049 Accuracy: 0.8819 Time: 8.89904  | Val Loss: 0.8357 Accuracy: 0.8731\n",
      "Epoch: 58/100 Train Loss: 0.7953 Accuracy: 0.8875 Time: 8.25707  | Val Loss: 0.8369 Accuracy: 0.8693\n",
      "Epoch: 59/100 Train Loss: 0.7806 Accuracy: 0.8925 Time: 8.74499  | Val Loss: 0.8443 Accuracy: 0.8670\n",
      "Epoch: 60/100 Train Loss: 0.7826 Accuracy: 0.8913 Time: 8.98653  | Val Loss: 0.8356 Accuracy: 0.8744\n",
      "Epoch: 61/100 Train Loss: 0.7790 Accuracy: 0.8960 Time: 8.74898  | Val Loss: 0.8220 Accuracy: 0.8754\n",
      "Epoch: 62/100 Train Loss: 0.7739 Accuracy: 0.8989 Time: 8.98004  | Val Loss: 0.8359 Accuracy: 0.8716\n",
      "Epoch: 63/100 Train Loss: 0.7665 Accuracy: 0.8987 Time: 8.26570  | Val Loss: 0.8296 Accuracy: 0.8716\n",
      "Epoch: 64/100 Train Loss: 0.7631 Accuracy: 0.9029 Time: 9.02288  | Val Loss: 0.8173 Accuracy: 0.8790\n",
      "Epoch: 65/100 Train Loss: 0.7510 Accuracy: 0.9096 Time: 8.94283  | Val Loss: 0.8215 Accuracy: 0.8790\n",
      "Epoch: 66/100 Train Loss: 0.7539 Accuracy: 0.9052 Time: 8.73925  | Val Loss: 0.8227 Accuracy: 0.8757\n",
      "Epoch: 67/100 Train Loss: 0.7505 Accuracy: 0.9083 Time: 9.06934  | Val Loss: 0.8267 Accuracy: 0.8754\n",
      "Epoch: 68/100 Train Loss: 0.7424 Accuracy: 0.9138 Time: 9.07008  | Val Loss: 0.8157 Accuracy: 0.8752\n",
      "Epoch: 69/100 Train Loss: 0.7386 Accuracy: 0.9155 Time: 9.25151  | Val Loss: 0.8092 Accuracy: 0.8820\n",
      "Epoch: 70/100 Train Loss: 0.7413 Accuracy: 0.9132 Time: 9.15200  | Val Loss: 0.8209 Accuracy: 0.8782\n",
      "Epoch: 71/100 Train Loss: 0.7339 Accuracy: 0.9135 Time: 9.03710  | Val Loss: 0.8160 Accuracy: 0.8762\n",
      "Epoch: 72/100 Train Loss: 0.7307 Accuracy: 0.9173 Time: 8.84146  | Val Loss: 0.8345 Accuracy: 0.8721\n",
      "Epoch: 73/100 Train Loss: 0.7318 Accuracy: 0.9190 Time: 8.84737  | Val Loss: 0.8186 Accuracy: 0.8780\n",
      "Epoch: 74/100 Train Loss: 0.7270 Accuracy: 0.9197 Time: 8.63488  | Val Loss: 0.8214 Accuracy: 0.8762\n",
      "Epoch: 75/100 Train Loss: 0.7275 Accuracy: 0.9184 Time: 8.74418  | Val Loss: 0.8200 Accuracy: 0.8772\n",
      "Epoch: 76/100 Train Loss: 0.7202 Accuracy: 0.9213 Time: 8.97058  | Val Loss: 0.8108 Accuracy: 0.8797\n",
      "Epoch: 77/100 Train Loss: 0.7204 Accuracy: 0.9248 Time: 9.51485  | Val Loss: 0.8106 Accuracy: 0.8808\n",
      "Epoch: 78/100 Train Loss: 0.7183 Accuracy: 0.9245 Time: 8.95174  | Val Loss: 0.8177 Accuracy: 0.8769\n",
      "Epoch: 79/100 Train Loss: 0.7197 Accuracy: 0.9220 Time: 8.97486  | Val Loss: 0.8135 Accuracy: 0.8833\n",
      "Epoch: 80/100 Train Loss: 0.7091 Accuracy: 0.9271 Time: 8.56200  | Val Loss: 0.8194 Accuracy: 0.8780\n",
      "Epoch: 81/100 Train Loss: 0.7109 Accuracy: 0.9279 Time: 9.04104  | Val Loss: 0.8143 Accuracy: 0.8841\n",
      "Epoch: 82/100 Train Loss: 0.7042 Accuracy: 0.9299 Time: 8.99693  | Val Loss: 0.8198 Accuracy: 0.8769\n",
      "Epoch: 83/100 Train Loss: 0.7084 Accuracy: 0.9264 Time: 9.01553  | Val Loss: 0.8133 Accuracy: 0.8803\n",
      "Epoch: 84/100 Train Loss: 0.7037 Accuracy: 0.9319 Time: 8.51892  | Val Loss: 0.8164 Accuracy: 0.8803\n",
      "Epoch: 85/100 Train Loss: 0.6993 Accuracy: 0.9333 Time: 8.85623  | Val Loss: 0.8131 Accuracy: 0.8785\n",
      "Epoch: 86/100 Train Loss: 0.7017 Accuracy: 0.9300 Time: 8.85852  | Val Loss: 0.8136 Accuracy: 0.8823\n",
      "Epoch: 87/100 Train Loss: 0.6951 Accuracy: 0.9372 Time: 8.43870  | Val Loss: 0.8122 Accuracy: 0.8825\n",
      "Epoch: 88/100 Train Loss: 0.7010 Accuracy: 0.9325 Time: 8.43451  | Val Loss: 0.8119 Accuracy: 0.8810\n",
      "Epoch: 89/100 Train Loss: 0.7032 Accuracy: 0.9306 Time: 8.53028  | Val Loss: 0.8100 Accuracy: 0.8815\n",
      "Epoch: 90/100 Train Loss: 0.6942 Accuracy: 0.9337 Time: 8.76169  | Val Loss: 0.8140 Accuracy: 0.8805\n",
      "Epoch: 91/100 Train Loss: 0.6960 Accuracy: 0.9336 Time: 9.05444  | Val Loss: 0.8099 Accuracy: 0.8792\n",
      "Epoch: 92/100 Train Loss: 0.6885 Accuracy: 0.9386 Time: 9.16553  | Val Loss: 0.8099 Accuracy: 0.8810\n",
      "Epoch: 93/100 Train Loss: 0.6950 Accuracy: 0.9346 Time: 9.05827  | Val Loss: 0.8070 Accuracy: 0.8815\n",
      "Epoch: 94/100 Train Loss: 0.6954 Accuracy: 0.9359 Time: 8.82993  | Val Loss: 0.8125 Accuracy: 0.8838\n",
      "Epoch: 95/100 Train Loss: 0.6965 Accuracy: 0.9324 Time: 8.44787  | Val Loss: 0.8079 Accuracy: 0.8831\n",
      "Epoch: 96/100 Train Loss: 0.6873 Accuracy: 0.9387 Time: 9.36304  | Val Loss: 0.8103 Accuracy: 0.8792\n",
      "Epoch: 97/100 Train Loss: 0.6974 Accuracy: 0.9345 Time: 8.89501  | Val Loss: 0.8103 Accuracy: 0.8808\n",
      "Epoch: 98/100 Train Loss: 0.6868 Accuracy: 0.9401 Time: 8.61391  | Val Loss: 0.8090 Accuracy: 0.8825\n",
      "Epoch: 99/100 Train Loss: 0.6909 Accuracy: 0.9362 Time: 9.04709  | Val Loss: 0.8085 Accuracy: 0.8813\n",
      "Epoch: 100/100 Train Loss: 0.6951 Accuracy: 0.9324 Time: 8.68646  | Val Loss: 0.8060 Accuracy: 0.8823\n",
      "#Parameter: 596010 Accuracy: 0.8820382165605095\n"
     ]
    }
   ],
   "source": [
    "net, width_multiplier_50 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier=0.5,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7248958f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 165154\n",
      "Epoch: 1/100 Train Loss: 2.1152 Accuracy: 0.2355 Time: 8.11122  | Val Loss: 2.0036 Accuracy: 0.2874\n",
      "Epoch: 2/100 Train Loss: 1.9361 Accuracy: 0.3454 Time: 8.24223  | Val Loss: 1.8441 Accuracy: 0.4056\n",
      "Epoch: 3/100 Train Loss: 1.7856 Accuracy: 0.4338 Time: 7.60871  | Val Loss: 1.7473 Accuracy: 0.4550\n",
      "Epoch: 4/100 Train Loss: 1.6572 Accuracy: 0.5032 Time: 6.90610  | Val Loss: 1.5035 Accuracy: 0.5743\n",
      "Epoch: 5/100 Train Loss: 1.5864 Accuracy: 0.5306 Time: 6.37236  | Val Loss: 1.4433 Accuracy: 0.6071\n",
      "Epoch: 6/100 Train Loss: 1.5382 Accuracy: 0.5512 Time: 6.25545  | Val Loss: 1.4592 Accuracy: 0.5941\n",
      "Epoch: 7/100 Train Loss: 1.4870 Accuracy: 0.5790 Time: 6.44015  | Val Loss: 1.4293 Accuracy: 0.6125\n",
      "Epoch: 8/100 Train Loss: 1.4343 Accuracy: 0.5961 Time: 5.34771  | Val Loss: 1.4163 Accuracy: 0.6237\n",
      "Epoch: 9/100 Train Loss: 1.4109 Accuracy: 0.6162 Time: 5.20524  | Val Loss: 1.3402 Accuracy: 0.6599\n",
      "Epoch: 10/100 Train Loss: 1.3858 Accuracy: 0.6271 Time: 5.11364  | Val Loss: 1.3053 Accuracy: 0.6576\n",
      "Epoch: 11/100 Train Loss: 1.3532 Accuracy: 0.6391 Time: 4.93254  | Val Loss: 1.2800 Accuracy: 0.6851\n",
      "Epoch: 12/100 Train Loss: 1.3283 Accuracy: 0.6501 Time: 4.91924  | Val Loss: 1.2555 Accuracy: 0.6854\n",
      "Epoch: 13/100 Train Loss: 1.3034 Accuracy: 0.6590 Time: 4.92075  | Val Loss: 1.2624 Accuracy: 0.6782\n",
      "Epoch: 14/100 Train Loss: 1.3003 Accuracy: 0.6602 Time: 5.06357  | Val Loss: 1.1980 Accuracy: 0.7017\n",
      "Epoch: 15/100 Train Loss: 1.2886 Accuracy: 0.6641 Time: 5.08770  | Val Loss: 1.2132 Accuracy: 0.7050\n",
      "Epoch: 16/100 Train Loss: 1.2587 Accuracy: 0.6817 Time: 5.10526  | Val Loss: 1.1698 Accuracy: 0.7246\n",
      "Epoch: 17/100 Train Loss: 1.2548 Accuracy: 0.6831 Time: 5.03313  | Val Loss: 1.1345 Accuracy: 0.7389\n",
      "Epoch: 18/100 Train Loss: 1.2331 Accuracy: 0.6935 Time: 4.86712  | Val Loss: 1.1672 Accuracy: 0.7248\n",
      "Epoch: 19/100 Train Loss: 1.2109 Accuracy: 0.6998 Time: 4.99414  | Val Loss: 1.1650 Accuracy: 0.7327\n",
      "Epoch: 20/100 Train Loss: 1.2059 Accuracy: 0.7047 Time: 5.00071  | Val Loss: 1.1461 Accuracy: 0.7345\n",
      "Epoch: 21/100 Train Loss: 1.1949 Accuracy: 0.7113 Time: 5.05940  | Val Loss: 1.1241 Accuracy: 0.7470\n",
      "Epoch: 22/100 Train Loss: 1.1853 Accuracy: 0.7178 Time: 4.98735  | Val Loss: 1.1219 Accuracy: 0.7539\n",
      "Epoch: 23/100 Train Loss: 1.1765 Accuracy: 0.7176 Time: 5.03553  | Val Loss: 1.1460 Accuracy: 0.7327\n",
      "Epoch: 24/100 Train Loss: 1.1529 Accuracy: 0.7292 Time: 4.94266  | Val Loss: 1.1049 Accuracy: 0.7580\n",
      "Epoch: 25/100 Train Loss: 1.1450 Accuracy: 0.7343 Time: 4.98587  | Val Loss: 1.0707 Accuracy: 0.7666\n",
      "Epoch: 26/100 Train Loss: 1.1493 Accuracy: 0.7283 Time: 4.92053  | Val Loss: 1.1379 Accuracy: 0.7401\n",
      "Epoch: 27/100 Train Loss: 1.1257 Accuracy: 0.7402 Time: 4.97059  | Val Loss: 1.1003 Accuracy: 0.7503\n",
      "Epoch: 28/100 Train Loss: 1.1273 Accuracy: 0.7410 Time: 4.94150  | Val Loss: 1.0554 Accuracy: 0.7804\n",
      "Epoch: 29/100 Train Loss: 1.1205 Accuracy: 0.7459 Time: 4.96498  | Val Loss: 1.0815 Accuracy: 0.7628\n",
      "Epoch: 30/100 Train Loss: 1.1232 Accuracy: 0.7422 Time: 5.12952  | Val Loss: 1.1074 Accuracy: 0.7552\n",
      "Epoch: 31/100 Train Loss: 1.1057 Accuracy: 0.7501 Time: 5.14946  | Val Loss: 1.0674 Accuracy: 0.7694\n",
      "Epoch: 32/100 Train Loss: 1.0959 Accuracy: 0.7481 Time: 4.97352  | Val Loss: 1.0767 Accuracy: 0.7702\n",
      "Epoch: 33/100 Train Loss: 1.0947 Accuracy: 0.7516 Time: 5.14958  | Val Loss: 1.0579 Accuracy: 0.7768\n",
      "Epoch: 34/100 Train Loss: 1.0895 Accuracy: 0.7562 Time: 4.95288  | Val Loss: 1.0511 Accuracy: 0.7799\n",
      "Epoch: 35/100 Train Loss: 1.0778 Accuracy: 0.7627 Time: 5.01970  | Val Loss: 1.0481 Accuracy: 0.7832\n",
      "Epoch: 36/100 Train Loss: 1.0677 Accuracy: 0.7676 Time: 5.02867  | Val Loss: 1.0334 Accuracy: 0.7916\n",
      "Epoch: 37/100 Train Loss: 1.0612 Accuracy: 0.7749 Time: 5.06488  | Val Loss: 1.1035 Accuracy: 0.7590\n",
      "Epoch: 38/100 Train Loss: 1.0636 Accuracy: 0.7683 Time: 5.01838  | Val Loss: 1.0361 Accuracy: 0.7865\n",
      "Epoch: 39/100 Train Loss: 1.0495 Accuracy: 0.7827 Time: 4.90484  | Val Loss: 1.0459 Accuracy: 0.7819\n",
      "Epoch: 40/100 Train Loss: 1.0523 Accuracy: 0.7726 Time: 4.97201  | Val Loss: 1.0162 Accuracy: 0.7873\n",
      "Epoch: 41/100 Train Loss: 1.0272 Accuracy: 0.7855 Time: 5.25984  | Val Loss: 1.0215 Accuracy: 0.7913\n",
      "Epoch: 42/100 Train Loss: 1.0438 Accuracy: 0.7813 Time: 4.91668  | Val Loss: 1.0053 Accuracy: 0.8018\n",
      "Epoch: 43/100 Train Loss: 1.0348 Accuracy: 0.7784 Time: 5.00769  | Val Loss: 1.0323 Accuracy: 0.7949\n",
      "Epoch: 44/100 Train Loss: 1.0201 Accuracy: 0.7896 Time: 4.85261  | Val Loss: 0.9990 Accuracy: 0.8033\n",
      "Epoch: 45/100 Train Loss: 1.0258 Accuracy: 0.7852 Time: 4.86299  | Val Loss: 1.0062 Accuracy: 0.8038\n",
      "Epoch: 46/100 Train Loss: 1.0160 Accuracy: 0.7963 Time: 5.06303  | Val Loss: 1.0067 Accuracy: 0.7997\n",
      "Epoch: 47/100 Train Loss: 1.0067 Accuracy: 0.7969 Time: 5.09917  | Val Loss: 0.9997 Accuracy: 0.8036\n",
      "Epoch: 48/100 Train Loss: 1.0033 Accuracy: 0.7972 Time: 4.98290  | Val Loss: 1.0197 Accuracy: 0.7987\n",
      "Epoch: 49/100 Train Loss: 0.9995 Accuracy: 0.7988 Time: 5.15511  | Val Loss: 0.9896 Accuracy: 0.8094\n",
      "Epoch: 50/100 Train Loss: 1.0021 Accuracy: 0.7985 Time: 5.04781  | Val Loss: 1.0315 Accuracy: 0.7890\n",
      "Epoch: 51/100 Train Loss: 0.9841 Accuracy: 0.8041 Time: 4.96554  | Val Loss: 0.9755 Accuracy: 0.8082\n",
      "Epoch: 52/100 Train Loss: 0.9810 Accuracy: 0.8085 Time: 4.95496  | Val Loss: 0.9817 Accuracy: 0.8122\n",
      "Epoch: 53/100 Train Loss: 0.9863 Accuracy: 0.8042 Time: 4.97946  | Val Loss: 0.9804 Accuracy: 0.8155\n",
      "Epoch: 54/100 Train Loss: 0.9792 Accuracy: 0.8100 Time: 5.05396  | Val Loss: 0.9863 Accuracy: 0.8130\n",
      "Epoch: 55/100 Train Loss: 0.9861 Accuracy: 0.8042 Time: 5.17976  | Val Loss: 0.9758 Accuracy: 0.8148\n",
      "Epoch: 56/100 Train Loss: 0.9670 Accuracy: 0.8101 Time: 5.01946  | Val Loss: 0.9902 Accuracy: 0.8094\n",
      "Epoch: 57/100 Train Loss: 0.9705 Accuracy: 0.8135 Time: 5.00861  | Val Loss: 0.9799 Accuracy: 0.8115\n",
      "Epoch: 58/100 Train Loss: 0.9639 Accuracy: 0.8148 Time: 5.03831  | Val Loss: 0.9621 Accuracy: 0.8168\n",
      "Epoch: 59/100 Train Loss: 0.9587 Accuracy: 0.8200 Time: 4.90463  | Val Loss: 0.9749 Accuracy: 0.8191\n",
      "Epoch: 60/100 Train Loss: 0.9486 Accuracy: 0.8228 Time: 4.88301  | Val Loss: 0.9538 Accuracy: 0.8247\n",
      "Epoch: 61/100 Train Loss: 0.9551 Accuracy: 0.8169 Time: 4.89406  | Val Loss: 0.9703 Accuracy: 0.8183\n",
      "Epoch: 62/100 Train Loss: 0.9536 Accuracy: 0.8146 Time: 4.80115  | Val Loss: 0.9515 Accuracy: 0.8262\n",
      "Epoch: 63/100 Train Loss: 0.9418 Accuracy: 0.8275 Time: 4.89726  | Val Loss: 0.9818 Accuracy: 0.8120\n",
      "Epoch: 64/100 Train Loss: 0.9420 Accuracy: 0.8238 Time: 4.85310  | Val Loss: 0.9554 Accuracy: 0.8222\n",
      "Epoch: 65/100 Train Loss: 0.9431 Accuracy: 0.8264 Time: 4.79344  | Val Loss: 0.9720 Accuracy: 0.8138\n",
      "Epoch: 66/100 Train Loss: 0.9286 Accuracy: 0.8309 Time: 4.88930  | Val Loss: 0.9567 Accuracy: 0.8239\n",
      "Epoch: 67/100 Train Loss: 0.9393 Accuracy: 0.8262 Time: 4.80822  | Val Loss: 0.9413 Accuracy: 0.8296\n",
      "Epoch: 68/100 Train Loss: 0.9298 Accuracy: 0.8289 Time: 4.92067  | Val Loss: 0.9506 Accuracy: 0.8206\n",
      "Epoch: 69/100 Train Loss: 0.9311 Accuracy: 0.8274 Time: 4.97403  | Val Loss: 0.9542 Accuracy: 0.8247\n",
      "Epoch: 70/100 Train Loss: 0.9288 Accuracy: 0.8301 Time: 5.00152  | Val Loss: 0.9532 Accuracy: 0.8245\n",
      "Epoch: 71/100 Train Loss: 0.9228 Accuracy: 0.8359 Time: 4.81076  | Val Loss: 0.9445 Accuracy: 0.8293\n",
      "Epoch: 72/100 Train Loss: 0.9080 Accuracy: 0.8403 Time: 4.90134  | Val Loss: 0.9467 Accuracy: 0.8280\n",
      "Epoch: 73/100 Train Loss: 0.9247 Accuracy: 0.8347 Time: 5.00463  | Val Loss: 0.9425 Accuracy: 0.8306\n",
      "Epoch: 74/100 Train Loss: 0.9108 Accuracy: 0.8368 Time: 4.87290  | Val Loss: 0.9407 Accuracy: 0.8321\n",
      "Epoch: 75/100 Train Loss: 0.9112 Accuracy: 0.8393 Time: 4.86230  | Val Loss: 0.9410 Accuracy: 0.8346\n",
      "Epoch: 76/100 Train Loss: 0.9094 Accuracy: 0.8422 Time: 4.92493  | Val Loss: 0.9372 Accuracy: 0.8311\n",
      "Epoch: 77/100 Train Loss: 0.9070 Accuracy: 0.8397 Time: 4.87020  | Val Loss: 0.9463 Accuracy: 0.8293\n",
      "Epoch: 78/100 Train Loss: 0.9101 Accuracy: 0.8350 Time: 4.94546  | Val Loss: 0.9419 Accuracy: 0.8288\n",
      "Epoch: 79/100 Train Loss: 0.9027 Accuracy: 0.8442 Time: 5.02645  | Val Loss: 0.9363 Accuracy: 0.8336\n",
      "Epoch: 80/100 Train Loss: 0.9131 Accuracy: 0.8361 Time: 4.96611  | Val Loss: 0.9440 Accuracy: 0.8321\n",
      "Epoch: 81/100 Train Loss: 0.9056 Accuracy: 0.8381 Time: 4.94332  | Val Loss: 0.9376 Accuracy: 0.8329\n",
      "Epoch: 82/100 Train Loss: 0.8923 Accuracy: 0.8511 Time: 5.06184  | Val Loss: 0.9377 Accuracy: 0.8313\n",
      "Epoch: 83/100 Train Loss: 0.8990 Accuracy: 0.8418 Time: 4.88924  | Val Loss: 0.9389 Accuracy: 0.8339\n",
      "Epoch: 84/100 Train Loss: 0.8932 Accuracy: 0.8494 Time: 5.03364  | Val Loss: 0.9312 Accuracy: 0.8387\n",
      "Epoch: 85/100 Train Loss: 0.8862 Accuracy: 0.8524 Time: 5.04806  | Val Loss: 0.9326 Accuracy: 0.8352\n",
      "Epoch: 86/100 Train Loss: 0.8945 Accuracy: 0.8467 Time: 4.91474  | Val Loss: 0.9345 Accuracy: 0.8341\n",
      "Epoch: 87/100 Train Loss: 0.8888 Accuracy: 0.8478 Time: 5.04829  | Val Loss: 0.9353 Accuracy: 0.8375\n",
      "Epoch: 88/100 Train Loss: 0.8908 Accuracy: 0.8498 Time: 4.90974  | Val Loss: 0.9384 Accuracy: 0.8377\n",
      "Epoch: 89/100 Train Loss: 0.8881 Accuracy: 0.8496 Time: 4.84752  | Val Loss: 0.9331 Accuracy: 0.8344\n",
      "Epoch: 90/100 Train Loss: 0.8871 Accuracy: 0.8489 Time: 4.87867  | Val Loss: 0.9341 Accuracy: 0.8336\n",
      "Epoch: 91/100 Train Loss: 0.8918 Accuracy: 0.8473 Time: 4.87730  | Val Loss: 0.9319 Accuracy: 0.8341\n",
      "Epoch: 92/100 Train Loss: 0.8912 Accuracy: 0.8445 Time: 5.01313  | Val Loss: 0.9334 Accuracy: 0.8359\n",
      "Epoch: 93/100 Train Loss: 0.8839 Accuracy: 0.8505 Time: 4.85318  | Val Loss: 0.9330 Accuracy: 0.8359\n",
      "Epoch: 94/100 Train Loss: 0.8891 Accuracy: 0.8442 Time: 4.91746  | Val Loss: 0.9288 Accuracy: 0.8341\n",
      "Epoch: 95/100 Train Loss: 0.8923 Accuracy: 0.8459 Time: 4.91766  | Val Loss: 0.9294 Accuracy: 0.8392\n",
      "Epoch: 96/100 Train Loss: 0.8827 Accuracy: 0.8480 Time: 4.96251  | Val Loss: 0.9366 Accuracy: 0.8344\n",
      "Epoch: 97/100 Train Loss: 0.8913 Accuracy: 0.8497 Time: 4.93379  | Val Loss: 0.9330 Accuracy: 0.8364\n",
      "Epoch: 98/100 Train Loss: 0.8907 Accuracy: 0.8502 Time: 4.89219  | Val Loss: 0.9320 Accuracy: 0.8362\n",
      "Epoch: 99/100 Train Loss: 0.8772 Accuracy: 0.8523 Time: 4.89623  | Val Loss: 0.9309 Accuracy: 0.8364\n",
      "Epoch: 100/100 Train Loss: 0.8947 Accuracy: 0.8458 Time: 4.93222  | Val Loss: 0.9316 Accuracy: 0.8354\n",
      "#Parameter: 165154 Accuracy: 0.8356687898089172\n"
     ]
    }
   ],
   "source": [
    "net, width_multiplier_25 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    width_multiplier=0.25,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6c2d68e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JCgrivvvu4/LLL/dJDCIiIo2F2vpzg5JqEZEmqmvXriQkJJCWluYpS0tL4+qrr6Zjx46sWbOmWnlKSspJh4SlpqbSrl07goODufbaazly5Ei1c9OnT2fz5s0YhoFhGKSmpnrO5+TkcO211xIcHEznzp1ZunTpaWMOCQnh9ddf56677iI+Pv6sfwYiIiJNmdr6c4OSahGRs1Ba6Tjlq9zu9Hrduho6dCgrVqzwHK9YsYKhQ4eSnJzsKa+srGTt2rWkpKSccP1XX33F7bffzj333EN6ejopKSk888wznvPjxo3jwQcfpGfPnmRmZpKZmcm4ceM856dPn86NN97Ili1bGDNmDL/85S/Jzc2t8/cQERHxFbX1autrYvV1AA3JVVlJ1h+exJGbS5tX5mAJDPR1SCLSyPV48pNTnkvp2pJ5Ey/wHJ//x88o+1mDetSFSdEsuHuw5/ji51aQW1J5Qr3d/3dFneIbOnQoDzzwAA6Hg7KyMjZt2sSll16K0+lkzpw5AKxbt46ysjJSUlLYu3dvtetffvllRo4cyaOPPgpAly5dWLNmDcuWLQMgKCiI0NBQrFbrSZ82T5gwgZtvvhmAZ599lldeeYWvv/6aUaNG1el7iIiI+IraerX1NWlWPdWGzUbBRx9Rsno1zmb29EREmqeUlBRKSkpYv349q1evpkuXLsTGxpKcnMz69espKSkhLS2Ndu3a0aFDhxOu37p1K4MHD65W9vPj0+nTp4/nfUhICGFhYWRnZwPQs2dPQkNDCQ0NZfTo0Wf4DUVO9NYXGTywIJ21Px6pubKISCOntt73mlVPtWEYWKOjcRw6hONILraEBF+HJCKN3A9PjzzlOcvPFgD55g+nXozj53W/+P2Jw7PORKdOnWjTpg0rVqwgLy+P5ORkAOLj40lKSuLLL79kxYoVXHbZZSe93jTNs/p8m81W7dgwDFwuFwAfffQRdrsdcD8FF/GWtT8d4dMfDjEwMZrBHWN8HY6INHJq609PbX0zS6oB/GLcSbUzV0+vReTsBfvX/n+j9VW3JkcXJcnLy+Phhx/2lCcnJ/PJJ5+wbt06Jk6ceNJre/Towbp166qV/fzY398fp/PkQ91Op3379nW+RqQ2/P3cA/HsTpePIxGRpkBtvdr6mjS7pNoaHUMF4Dii4d8i0jykpKQwadIk7Ha75+k1uBva3/72t5SXl5904RKA++67jyFDhjBz5kyuueYali9f7pljdVRiYiIZGRmkp6fTpk0bwsLCCAgIOON4f/jhByorK8nNzaWoqIj09HQAr+2PKU2f1c/dG6SkWkSaC7X1vtWs5lQDWGOiAdRTLSLNRkpKCmVlZXTq1Im4uDhPeXJyMkVFRXTs2JG2bdue9NpBgwbx5ptv8sorr9CvXz+WL1/OE088Ua3O9ddfz6hRo0hJSaFly5bMnz//rOIdM2YM/fv35z//+Q9paWn079+f/v37n9U9pXmxVfVUO1xnN6RRRKSxUFvvW4Z5toPoG0BhYSEREREUFBQQHh5+Vvc69NxMcufNI3riROJ+/4iXIhSRpqy8vJyMjAySkpII1K4BzcrpfvfebJvEuz/PqYu2MP/rfTw4vAv3DuvspQhFpKlTe988eaOtV0+1iIiINClHe6rt6qkWEZEG0OySar9o9yqgmlMtIiLSNFktWqhMREQaTrNbqGxt2fckApU5h30dioiIiNSDKSO6cO9lnQjy9/N1KCIi0gw0u57qd7L+A4D9SI6PIxEREZH6EBpgJSrEn0CbkmoREal/zS6pdkWGAWDm5Z/1RuciIiIiIiLSvDW7pJpI96pthsOJq6jIx8GIiIiIt63acZjHFn/Lvzbs83UoIiLSDDS7pDogOIxSf/d7xxGtAC4iItLUbM0s5P2v9rL2R7XzIiJS/5pdUh3mH0ZhsPu9M1crgIuIiDQ12lJLREQaUrNLqkP9QykIcb9XT7WIiDQnM2bMYODAgYSFhREbG8s111zD9u3bT3tNWloahmGc8Nq2bVu1egsXLqRHjx4EBATQo0cPFi9eXJ9f5bRsfgYADm2pJSIiDaD5JdW2UAqD3Y2teqpFRKo7mkDl5+efsk5qaiqRkZE13sswDJYsWeK12OTsrVy5kkmTJrFu3To+/fRTHA4HI0aMoKSkpMZrt2/fTmZmpufVuXNnz7m1a9cybtw4br31VjZv3sytt97KjTfeyFdffVWfX+eUrH7ap1pE5FTU1ntfs0uqb+l2C/26JgPqqRaRpu2NN94gLCwMh8PhKSsuLsZms3HJJZdUq7t69WoMwyAhIYHMzEwiIiJq/TnTpk2jX79+3gqbb7/9luTkZIKCgmjdujVPP/30aXdrOFVPqmEYrF+/3lPvZOffeOMNr8XdGCxbtowJEybQs2dP+vbty7x589i7dy/ffPNNjdfGxsYSHx/vefn5Hduuavbs2QwfPpypU6fSrVs3pk6dyrBhw5g9e3Y9fptT8wz/dmr4t4g0bWrrz422vtkl1R0iOxDfpisAziPqqRaRpislJYXi4mI2bNjgKVu9ejXx8fGsX7+e0tJST3laWhoJCQl06dKF+Ph4DMPwRcgUFhYyfPhwEhISWL9+Pa+88gqzZs3ixRdfPOU1Q4YMqdaDmpmZyZ133kliYiIDBgyoVnfevHnV6t122231/ZXOaQUFBQBER0fXWLd///60atWKYcOGsWLFimrn1q5dy4gRI6qVjRw5kjVr1ngv2DrwDP92qadaRJo2tfXnRlvf7JJqAGt0DAAODf8WkSasa9euJCQkkJaW5ilLS0vj6quvpmPHjtUSnrS0NFJSUk46JCw1NZV27doRHBzMtddey5HjRvmkpqYyffp0Nm/e7HkinJqa6jmfk5PDtddeS3BwMJ07d2bp0qWnjfm9996jvLyc1NRUevXqxXXXXcdjjz3Giy++eMon2P7+/tV6UGNiYli6dCm33377CX8wREZGVqsbFBRUi59k02SaJlOmTOHiiy+mV69ep6zXqlUr/vrXv7Jw4UIWLVpE165dGTZsGKtWrfLUycrKIi4urtp1cXFxZGVlnfK+FRUVFBYWVnt5i6en2qGeahFp2tTWnxttfbNLqg8UH+DrCveiLE4N/xaRM2WaUFnim9dphkf93NChQ6v1Kq5YsYKhQ4eSnJzsKa+srGTt2rWkpKSccP1XX33F7bffzj333EN6ejopKSk888wznvPjxo3jwQcfpGfPnp4nwuPGjfOcnz59OjfeeCNbtmxhzJgx/PKXvyT3NA80165dS3JyMgEBAZ6ykSNHcvDgQXbv3l2r77x06VJycnKYMGHCCecmT55MixYtGDhwIG+88QauZtyTOXnyZLZs2cL8+fNPW69r167cddddnHfeeQwePJjXXnuNK664glmzZlWr9/M/akzTPG0vyIwZM4iIiPC82rZte+Zf5meGdm3JF79P4Y1bz/faPUWkGVJbD6itrw1rvX/COWZX3i5SDy7mKcCRp55qETlD9lJ4NsE3n/3YQfAPqVXVoUOH8sADD+BwOCgrK2PTpk1ceumlOJ1O5syZA8C6desoKysjJSWFvXv3Vrv+5ZdfZuTIkTz66KMAdOnShTVr1rBs2TIAgoKCCA0NxWq1Eh8ff8LnT5gwgZtvvhmAZ599lldeeYWvv/6aUaNGnTTerKwsEhMTq5Ud7QHNysoiKSmpxu88d+5cRo4ceUKS9sc//pFhw4YRFBTE//73Px588EFycnJ44oknarxnU3PvvfeydOlSVq1aRZs2bep8/aBBg3j33Xc9x/Hx8Sf0SmdnZ5/Qe328qVOnMmXKFM9xYWGh1xLrYH8rwf7N7k8cEfE2tfWA2vraaHY91aH+oRSGVK3+rTnVItLEpaSkUFJSwvr161m9ejVdunQhNjaW5ORk1q9fT0lJCWlpabRr144OHTqccP3WrVsZPHhwtbKfH59Onz59PO9DQkIICwsjOzsbgJ49exIaGkpoaCijR4/21DtZj+fJyk9m//79fPLJJ9xxxx0nnHviiScYPHgw/fr148EHH+Tpp5/m+eefr/V3aQpM02Ty5MksWrSIzz//vFZ/uJzMpk2baNWqled48ODBfPrpp9XqLF++nCFDhpzyHgEBAYSHh1d7iYhI3amtP8ZXbX2ze4wbagulINj93pmfj+lwYFib3Y9BRM6WLdj9FNlXn11LnTp1ok2bNqxYsYK8vDySk927H8THx5OUlMSXX37JihUruOyyy056/elW4qxVqDZbtWPDMDzDsD766CPsdjuAZ77TqXo8gdP2eh41b948YmJiuOqqq2qsO2jQIAoLCzl06FCt7t0UTJo0iffff58PPviAsLAwz886IiLC8zuYOnUqBw4c4O233wbcK3snJibSs2dPKisreffdd1m4cCELFy703Pf+++/n0ksv5bnnnuPqq6/mgw8+4LPPPuOLL75o+C8J7Mst5e9rdhMZbGPyZZ1rvkBE5GTU1tcuVLX1zTCp9g+lKAhcgMU0cebnY23RwtdhiUhjYxi1Hpbla0cXJcnLy+Phhx/2lCcnJ/PJJ5+wbt06Jk6ceNJre/Towbp166qV/fzY398fp9NZ57jat29/QtngwYN57LHHqKysxN/fH3D3eCYkJJwwVOznTNNk3rx5jB8//oQG/mQ2bdpEYGBgrfbhbCpef/11wD1U8Hjz5s3zzEvLzMysNjSwsrKShx56iAMHDhAUFETPnj358MMPGTNmjKfOkCFD+Mc//sETTzzBH/7wBzp27MiCBQu48MIL6/07nUx2UQVvfpFBu+hgJdUicubU1nuorT+95jf82xaKaTEoqnr449AQcBFp4lJSUvjiiy9IT0/3PL0Gd0P7t7/9jfLy8pMuXAJw3333sWzZMmbOnMmOHTt49dVXPXOsjkpMTCQjI4P09HRycnKoqKg441hvueUWAgICmDBhAt999x2LFy/m2WefZcqUKZ4hYV9//TXdunXjwIED1a79/PPPycjIOOlwsP/85z/87W9/47vvvuPHH3/kzTff5PHHH+fXv/51tYVSmjrTNE/6On6hl9TU1GqryD7yyCPs2rWLsrIycnNzWb16dbWE+qgbbriBbdu2UVlZydatW7nuuusa4BudnGdLLWfzXYhORJoXtfW+bevrlFTPmDGDgQMHEhYWRmxsLNdccw3bt2+v8bqVK1dy/vnnExgYSIcOHRpkA+5TCbWFAhwbAp6rFcBFpGlLSUmhrKyMTp06VRv6lJycTFFRER07djzlAlGDBg3izTff5JVXXqFfv34sX778hMU+rr/+ekaNGkVKSgotW7ascTXp04mIiODTTz9l//79DBgwgHvuuYcpU6ZUW9CqtLSU7du3e4aTHTV37lyGDBlC9+7dT7ivzWbjtddeY/DgwfTp04eXX36Zp59+mhdeeOGMY5Vzl9VStaWWS1tqiUjzoLbet229YdZhEP2oUaO46aabGDhwIA6Hg8cff5xvv/2WH374gZCQkw+NyMjIoFevXtx1113cfffdfPnll9xzzz3Mnz+f66+/vlafW1hYSEREBAUFBV5ZyOTC9y7kobeL6LXHJGHWLCKuvOKs7ykiTVd5eTkZGRkkJSURGBjo63CkAZ3ud+/ttqm58+bPc1d2EZe/uIrIYBvpT47wUoQi0tSpvW+evNHW12lO9c+HAcybN4/Y2Fi++eYbLr300pNe88Ybb9CuXTtmz54NQPfu3dmwYQOzZs2qdVLtbS8MfYHwL96EPV+pp1pERKSJOdpT7XCqp1pEROrfWc2pLigoACA6OvqUddauXcuIEdWfEo8cOZINGzac0J1/VEVFBYWFhdVe3nRx64uJbe1euERzqkVERJoWa9WcarvmVIuISAM446TaNE2mTJnCxRdfTK9evU5ZLysr64Tly+Pi4nA4HOTk5Jz0mhkzZhAREeF5nWr8/9mwxrgfBKinWkREpGnx96vqqdacahERaQBnvKXW5MmT2bJlS632oKzr5t5Tp06tNlG9sLDQq4n115lfU1D5E21QT7WIiEhTEx3iz6cPXIrVr9ltciIiIj5wRkn1vffey9KlS1m1ahVt2rQ5bd1Tbe5ttVqJiYk56TUBAQH1uuz5gu0LyMtexsOA84h6qkVERJoSq5+FznFhvg5DRESaiTol1aZpcu+997J48WLS0tJISkqq8ZrBgwfzn//8p1rZ8uXLGTBgQK027PYqlxP+fhVhrkx2B1ftYZmrnmoRERERERE5M3UaFzVp0iTeffdd3n//fcLCwsjKyiIrK4uysjJPnalTpzJ+/HjP8W9+8xv27NnDlClT2Lp1K2+99RZz587loYce8t63qC2LH+xfT2jJkWP7VKunWkREpEkxTZMXl2/nuWXbKK10+DocERFp4uqUVL/++usUFBQwdOhQWrVq5XktWLDAUyczM5O9e/d6jpOSkvjoo49IS0ujX79+/PGPf2TOnDk+206LgDBCXS4Kq7bVdpWW4jruoYCIiIg0fnM+38XraT9SUuH0dSgiItLE1Xn4d01SU1NPKEtOTmbjxo11+aj6ExhOqOMwZf7gsFqwOlw4c3OxtG7t68hERETECwzDwN/PQqXTpW21RESk3jW/ZTGreqoxDMrC3HO6Na9aRMQtLS0NwzDIz88/ZZ3U1FQiIyNrvJdhGCxZssRrsYnUxdG9qh1ObaslInI8tfXe1wyT6nDCXO6n1sUhfgA4NK9aRJqgN954g7CwMByOY3NKi4uLsdlsXHLJJdXqrl69GsMwSEhIIDMzk4iIiFp/zrRp0+jXr5+3wubbb78lOTmZoKAgWrduzdNPP13jSKnExEQMw6j2evTRR70WkzQ+Vos7qa5UT7WINGFq68+Ntv6M96lutALC6VNRwey2VxHX+ifYn45Te1WLSBOUkpJCcXExGzZsYNCgQYC7QY2Pj2f9+vWUlpYSHOxetTEtLY2EhAS6dOniy5ApLCxk+PDhpKSksH79enbs2MGECRMICQnhwQcfPO21Tz/9NHfddZfnODQ0tL7DlXOYv9Xdb+BwKakWkaZLbf250dY3w57qMFo6XQwLiCMqvj0Ajlz1VItI09O1a1cSEhJIS0vzlKWlpXH11VfTsWNH1qxZU608JSXlpEPCUlNTadeuHcHBwVx77bUcOW50T2pqKtOnT2fz5s2ep8bHr62Rk5PDtddeS3BwMJ07d2bp0qWnjfm9996jvLyc1NRUevXqxXXXXcdjjz3Giy++WOMT7LCwMOLj4z2vc6WhFd+wWtx/4tgdGv4tIk2X2vpzo61vlkk1ABVF+MXEAKinWkTOWKm99JSvCmdFreuWO8prVbeuhg4dyooVKzzHK1asYOjQoSQnJ3vKKysrWbt2LSkpKSdc/9VXX3H77bdzzz33kJ6eTkpKCs8884zn/Lhx43jwwQfp2bMnmZmZZGZmMm7cOM/56dOnc+ONN7JlyxbGjBnDL3/5S3JPs47F2rVrSU5OJiAgwFM2cuRIDh48yO7du0/7XZ977jliYmLo168ff/rTn6isrKzx5yNN19E51Xb1VIvIWVJbr7a+Js1u+PeX+yu5APgw93vCnS1ohXqqReTMXfj+hac8d0nrS3jt8tc8x0P/OZQyx8m38BsQN4B5o+Z5jkctHEVeRd4J9b697ds6xTd06FAeeOABHA4HZWVlbNq0iUsvvRSn08mcOXMAWLduHWVlZaSkpFTbEhHg5ZdfZuTIkZ45S126dGHNmjUsW7YMgKCgIEJDQ7FarcTHx5/w+RMmTODmm28G4Nlnn+WVV17h66+/ZtSoUSeNNysri8TExGplcXFxnnNJSUknve7+++/nvPPOIyoqiq+//pqpU6eSkZHBm2++WcuflDQ1b942AKfLJKlFiK9DEZFGTm292vqaNLue6g1ZDkzgDyXfsijnf4B6qkWk6UpJSaGkpIT169ezevVqunTpQmxsLMnJyaxfv56SkhLS0tJo164dHTp0OOH6rVu3Mnjw4GplPz8+nT59+njeh4SEEBYWRnZ2NgA9e/YkNDSU0NBQRo8e7alnGEa1exwdCvbz8uM98MADJCcn06dPH+68807eeOMN5s6dW234mjQv3eLD6ZkQQbB/s+s/EJFmRm2979v6ZtfSOKyh+NvB3zQoqHp4rS21RORMfXXLV6c852fxq3acdmPaKetajOrPOJddv+ys4jqqU6dOtGnThhUrVpCXl0dycjIA8fHxJCUl8eWXX7JixQouu+yyk15f09ymmthstmrHhmHgqhqO+9FHH2G32wH3U/CjcWVlZVW75mjDfPQpdm0cXaxl165dxFRN9RERETkTautPT219M0yqnf5hYIcQDAqC3U9CnOfA0w0RaZyCbcE+r1uTo4uS5OXl8fDDD3vKk5OT+eSTT1i3bh0TJ0486bU9evRg3bp11cp+fuzv74/T6axzXO3btz+hbPDgwTz22GNUVlbi7+8PwPLly0lISDhhqNjpbNq0CYBWrVrVOS5pGhZt3M+BvDJG925Fp9hzYyEbEWmc1Narra9Jsxv+bfq7FyoLdkHhcT3VphYyEZEmKiUlhS+++IL09HTP02twN7R/+9vfKC8vP+nCJQD33Xcfy5YtY+bMmezYsYNXX33VM8fqqMTERDIyMkhPTycnJ4eKioqT3qs2brnlFgICApgwYQLfffcdixcv5tlnn2XKlCmeIWFff/013bp148CBA4B7wZOXXnqJ9PR0MjIy+Oc//8ndd9/NVVddRbt27c44Fmnc3v9qLy98uoOdh4p8HYqISL1TW+9bzS6pJiAcgFCXi/yja5c4HDiPW1JeRKQpSUlJoaysjE6dOlUbVpWcnExRUREdO3akbdu2J7120KBBvPnmm7zyyiv069eP5cuX88QTT1Src/311zNq1ChSUlJo2bIl8+fPP+NYIyIi+PTTT9m/fz8DBgzgnnvuYcqUKUyZMsVTp7S0lO3bt3uGkwUEBLBgwQKGDh1Kjx49ePLJJ7nrrrvOKg5p/Gx+VVtqubSllog0fWrrfcswz3YQfQMoLCwkIiKCgoICwsPDz+pes/7+bx7KuIOJrVqzIdCP918PxJpfTNKSxQR26+aliEWkKSkvLycjI4OkpCQCAwN9HY40oNP97r3ZNon3f563zv2K1TtzeOEXfbn+/DZeiFBEmjq1982TN9r6ZtdTbQly/zDCXA4A7FHueVaOqsnxIiIi0vj5V/VUOzS9S0RE6lmzS6onpLiXfJ+Yn8fs5BcIS3CPwVdSLSIi0nRY/dzz8uzOc35AnoiINHLNLqmOjnYvt96/opJhsQMIrUqq7YcO+TIsERER8SLPnGqneqpFRKR+NbukGj8bWN17pFFRhLVlLACO7MM+DEpERES86WhS7VBPtYiI1LNmt0/1tqxCWhNMvrWSb3cvIy6whGA0/FtERKQpmZTSiV9e2I620d7bB1ZERORkml1SnZlfjq3Sn00RgUzf8iq3lfbkCpRUi4iINCWdYkN9HYKIiDQTzW74d1iglWKCCK3at/JwiBNQUi0iIiIiIiJ11+x6qkMDreSYQYRWbbFxKLgSAMeRI5gOB4a12f1IREREmpwNu3PZvL+AngnhDOoQ4+twRESkCWuGPdU2igk+llT7l4OfH7hcOI4c8XF0IiIi4g2fbc3mj//9gU9/0O4eIiJSv5phUu0e/h1WlVQXOUuwtmgBaAi4iEhaWhqGYZCfn3/KOqmpqURGRtZ4L8MwWLJkiddiE6kLW9U+1Q5tqSUiUo3aeu9rdkl1iL+VIvPYnOriymKscUe31VJSLSJNxxtvvEFYWBgOh8NTVlxcjM1m45JLLqlWd/Xq1RiGQUJCApmZmURERNT6c6ZNm0a/fv28FTbffvstycnJBAUF0bp1a55++mlM89TbIu3evZs77riDpKQkgoKC6NixI0899RSVlZXV6hmGccLrjTfe8Frccm45uqVWpbbUEpEmTG39udHWN7sJxH4Wgwq/EE9PtdN0YlFPtYg0QSkpKRQXF7NhwwYGDRoEuBvU+Ph41q9fT2lpKcHB7u2G0tLSSEhIoEuXLr4MmcLCQoYPH05KSgrr169nx44dTJgwgZCQEB588MGTXrNt2zZcLhd/+ctf6NSpE9999x133XUXJSUlzJo1q1rdefPmMWrUKM9xXf6gkMbFqp5qEWkG1NafG219s+upBrjxoh4EmSZPB3Zi9tDZ2GLdPdV2JdUi0oR07dqVhIQE0tLSPGVpaWlcffXVdOzYkTVr1lQrT0lJOemQsNTUVNq1a0dwcDDXXnstR45bfyI1NZXp06ezefNmzxPh1NRUz/mcnByuvfZagoOD6dy5M0uXLj1tzO+99x7l5eWkpqbSq1cvrrvuOh577DFefPHFUz7BHjVqFPPmzWPEiBF06NCBq666ioceeohFixadUDcyMpL4+HjPKygoqIafojRWNov7TxyHSz3VItJ0qa0/N9r6ZplUR0e3wACudQUxrP0w/OPjAfVUi0jtmaaJq7TUJ6/TDY/6uaFDh7JixQrP8YoVKxg6dCjJycme8srKStauXUtKSsoJ13/11Vfcfvvt3HPPPaSnp5OSksIzzzzjOT9u3DgefPBBevbsSWZmJpmZmYwbN85zfvr06dx4441s2bKFMWPG8Mtf/pLc3NxTxrt27VqSk5MJCAjwlI0cOZKDBw+ye/fuWn/vgoICoqOjTyifPHkyLVq0YODAgbzxxhu4XOrFbKqOzqmuVE+1iJwhtfVuautr1uyGfwMQEOb+t6IQAGvLo3OqD/sqIhFpZMyyMrafd75PPrvrxm8wqoZy1WTo0KE88MADOBwOysrK2LRpE5deeilOp5M5c+YAsG7dOsrKykhJSWHv3r3Vrn/55ZcZOXIkjz76KABdunRhzZo1LFu2DICgoCBCQ0OxWq3EVz2gPN6ECRO4+eabAXj22Wd55ZVX+Prrr6sNyzpeVlYWiYmJ1cri4uI855KSkmr8zj/++COvvPIKL7zwQrXyP/7xjwwbNoygoCD+97//8eCDD5KTk8MTTzxR4z2l8bFWzanW8G8ROVNq69XW11az7Klec8AOwJelh/k442OKwt3PFhyHtO2GiDQtKSkplJSUsH79elavXk2XLl2IjY0lOTmZ9evXU1JSQlpaGu3ataNDhw4nXL9161YGDx5creznx6fTp08fz/uQkBDCwsLIrhoV1LNnT0JDQwkNDWX06NGeeoZhVLvH0af1Py8/mYMHDzJq1Ch+8YtfcOedd1Y798QTTzB48GD69evHgw8+yNNPP83zzz9f6+8ijctl3WJ5+/YL+N3lvp07KCJS39TWH+Ortr5Z9lSvPVDJEOA1WwlbVj3CnPYPEo+Gf4tI7RlBQXTd+I3PPru2OnXqRJs2bVixYgV5eXkkJycDEB8fT1JSEl9++SUrVqzgsssuO+n1dRl+djI2m63asWEYnmFYH330EXa7+yHn0flO8fHxZGVlVbvmaMN89Cn2qRw8eJCUlBQGDx7MX//61xpjGzRoEIWFhRw6dKjGe0vjkxAZREKk5syLyJlTW187auubaU+1JTAcgDCnE4CCqp5qZ34+rp8tyy4icjKGYWAJDvbJqzZPcY93dFGStLQ0hg4d6ilPTk7mk08+Yd26dSedYwXQo0cP1q1bV63s58f+/v44q/5/Whft27enU6dOdOrUidatWwPuJ+OrVq2qtkXG8uXLSUhIOGGo2PEOHDjA0KFDOe+885g3bx4WS83N26ZNmwgMDKzVPpxNxYwZMxg4cCBhYWHExsZyzTXXsH379tNes2jRIoYPH07Lli0JDw9n8ODBfPLJJ9XqpKamnnQbk/Ly8vr8OiIi9Upt/TFq60+vWSbV1iD3surhTvdTkwJ/B4a/P6B51SLS9KSkpPDFF1+Qnp7ueXoN7ob2b3/7G+Xl5adsaO+77z6WLVvGzJkz2bFjB6+++qpnjtVRiYmJZGRkkJ6eTk5ODhUVFWcc6y233EJAQAATJkzgu+++Y/HixTz77LNMmTLF8wfG119/Tbdu3Thw4ADgfmo9dOhQ2rZty6xZszh8+DBZWVnVnoL/5z//4W9/+xvfffcdP/74I2+++SaPP/44v/71r6stlNLUrVy5kkmTJrFu3To+/fRTHA4HI0aMoKSk5JTXrFq1iuHDh/PRRx/xzTffkJKSwtixY9m0aVO1euHh4Z4FbI6+AgMD6/srndL+vFLmf72XZd9l1VxZRKSRU1vv47bebAQKCgpMwCwoKPDK/V5f9o1pPhVu/nFOe7NXai/zz5v+bO4cdrn5Q9duZsk3G73yGSLSdJSVlZk//PCDWVZW5utQzkhGRoYJmN26datWvm/fPhMwO3bs6ClbsWKFCZh5eXmesrlz55pt2rQxg4KCzLFjx5qzZs0yIyIiPOfLy8vN66+/3oyMjDQBc968eaZpmiZgLl68uNpnRkREeM6fypYtW8xLLrnEDAgIMOPj481p06aZLpfrhBgzMjJM0zTNefPmmcBJX0d9/PHHZr9+/czQ0FAzODjY7NWrlzl79mzTbrefNpbT/e693Tb5QnZ2tgmYK1eurNN1PXr0MKdPn+45njdvXrX/Js6Et3+en36fZbb//X/Nq179wiv3E5GmrzG392rrfdvWG6Z5loPoG0BhYSEREREUFBQQHh5+1vebt3oXE/93Pi9FRfBWZAS/6v4rxs3eQtnGjbSePZvwUSO9ELWINBXl5eVkZGSQlJTk0543aXin+917u23yhV27dtG5c2e+/fZbevXqVatrXC4XiYmJPPLII0yePBlwD/++8847ad26NU6nk379+vHHP/6R/v371zoWb/8807ZnM2HeenomhPPhfZec9f1EpOlTe988eaOtb5bDv0ODAig2AwlzuZ8nFNuLscYe3VZLK4CLiEjTZ5omU6ZM4eKLL651Qg3wwgsvUFJSwo033ugp69atG6mpqSxdupT58+cTGBjIRRddxM6dO095n4qKCgoLC6u9vMlWtaWWXVtqiYhIPWuWq3+HBdooJohQl3tOdXFlMba4o0m1VgAXEZGmb/LkyWzZsoUvvvii1tfMnz+fadOm8cEHHxBb9TAa3KurDho0yHN80UUXcd555/HKK6949kj9uRkzZjB9+vQz/wI1sHn2qT7nB+SJiEgj1yx7qod0iiE6KoYLysv5Y9fx3NrjVk9PtV1JtYiINHH33nsvS5cuZcWKFbRp06ZW1yxYsIA77riDf/7zn1x++eWnrWuxWBg4cOBpe6qnTp1KQUGB57Vv3746fYeaWP3ci91UqqdaRETqWbPsqQ4PtEFIJB3yHXSI7Alx51EQ615ZTqt/i4hIU2WaJvfeey+LFy8mLS2NpKSkWl03f/58br/9dubPn88VV1xRq89JT0+nd+/ep6wTEBBQr6ux2izqqRYRkYbRLJNqAALC3P9WFAFgbanh3yJyeo1gXUfxsqb2O580aRLvv/8+H3zwAWFhYZ6tSCIiIggKCgLcPcgHDhzg7bffBtwJ9fjx43n55ZcZNGiQ55qgoCAiItxbVE6fPp1BgwbRuXNnCgsLmTNnDunp6fz5z3/2wbd0s1ndPdUOl3qqRaRumtr/++X0vPH7bpbDv8vtTnYWGJQaBqtytvC/vf87bqEyJdUiUp3NZgOgtLTUx5FIQzv6Oz/630Bj9/rrr1NQUMDQoUNp1aqV57VgwQJPnczMTPbu3es5/stf/oLD4WDSpEnVrrn//vs9dfLz8/n1r39N9+7dGTFiBAcOHGDVqlVccMEFDfr9jtc6Mog3fnU+L9zYz2cxiEjj4ufnB0BlZaWPI5GG5I22vs491atWreL555/nm2++ITMzk8WLF3PNNdec9pr33nuPmTNnsnPnTiIiIhg1ahSzZs0iJibmTOM+axsPuQgKtDBp/1ICMj/hq2tWAuAqLsZVUoIlJMRnsYnIucXPz4/IyEiyqx66BQcHYxiGj6OS+mSaJqWlpWRnZxMZGen5Q6uxq83T+NTU1GrHaWlpNV7z0ksv8dJLL51hVPUjLNDGqF7xvg5DRBoRq9VKcHAwhw8fxmazYbE0y/7HZsObbX2dk+qSkhL69u3LxIkTuf7662us/8UXXzB+/Hheeuklxo4dy4EDB/jNb37DnXfeyeLFi88o6LMVaPOj1AgipmrxkgpnBSX+TizBwbhKS7FnZxNQy3lmItI8xMe7/zjP1miWZiUyMtLzuxcRkabNMAxatWpFRkYGe/bs8XU40kC80dbXOakePXo0o0ePrnX9devWkZiYyH333QdAUlISd999NzNnzqzrR3tVpTWUINMkAhsF2MkszsQaF0dlRgaO7MNKqkWkmqMNbWxsLHa73dfhSAOw2WxNpoe6OSq3O/nk+yzsTpPrz2ut0SUiUiv+/v507txZQ8CbCW+19fW+UNmQIUN4/PHH+eijjxg9ejTZ2dn8+9//rtXqofXJYQuFSojDSgF2DhYfpENsbFVSrZ4oETk5Pz8/JVoijUC53cn9/0gH4Jp+CZ4ttkREamKxWAgMDPR1GNKI1PtEgSFDhvDee+8xbtw4/P39iY+PJzIykldeeeWU11RUVFBYWFjt5W1OWygAcU73j+BgyUEtViYiItJEWP2O/YnjcGklXxERqT/1nlT/8MMP3HfffTz55JN88803LFu2jIyMDH7zm9+c8poZM2YQERHhebVt29brcbn8wwGIc7jnVWcWZ2KNbQkoqRYREWnsbMf1TNud2lZLRETqT70n1TNmzOCiiy7i4Ycfpk+fPowcOZLXXnuNt956i8zMzJNeM3XqVAoKCjyvffv2eT+wqn2qW9kdgLun2na0p/qwkmoREZHGzHbcqr12p3qqRUSk/tT7nOrS0lKs1uofc3Q+4qm29ggICCAgIKBe4xp3UQ9YCJc5HbQc8jSdozpjLXMn7/ZDSqpFREQaM4vFwM9i4HSZONRTLSIi9ajOSXVxcTG7du3yHGdkZJCenk50dDTt2rVj6tSpHDhwgLfffhuAsWPHctddd/H6668zcuRIMjMz+d3vfscFF1xAQkKC975JHbWKiwOgU1kRnTpfC0BpnHuVP8ehQz6LS0RERLzDWpVUVyqpFhGRelTnpHrDhg2kpKR4jqdMmQLAbbfdRmpqKpmZmezdu9dzfsKECRQVFfHqq6/y4IMPEhkZyWWXXcZzzz3nhfDPQtXwbyqKwDTBMLBV7U9mP3QI0+XC0IbvIiIijZbNz0KFw0XI2lmQ8RHc/jEERfk6LBERaWLqnFQPHTr0lMO2AVJTU08ou/fee7n33nvr+lH16rsjLnoBuOxsOLiWvaVZXBp/Efj5gd2O4/BhbFW92SIiItL4/N/1vTFNiEh7HPIy4MBG6DTM12GJiEgT02y7YrdkO3GZ7pVBn/nqWZ5a8xQ7Cn/0JNL2gwd9GZ6IiIicpSv7JDC2bwKWymJ3gbPStwGJiEiT1GyT6rAgf4pxb+reKjAGgMySTKwJrQAl1SIiIk1GeaH7X0eFb+MQEZEmqdkm1aGBVooJAiAhIBKo2laravE0JdUiIiKN2xc7c1iWvgecVcm0kmoREakH9b6l1rkqPNBKkRkMRi6trKEAZBZnepJqxyn20BYREZHG4aml35F3OJNRgVUFTiXVIiLifc22pzos0Hasp9rP/e/BkoPYWlX1VB9QT7WIiEhjZvOzEGaUHitQT7WIiNSDZptUhwZU9VQDrQx/oHpPtYZ/i4iING5WP4MwlFSLiEj9arbDv8OOm1Md63SvAn6o9BCWVrEA2DX8W0REpFGz+VkINMqOFTjKfReMiIg0Wc02qQ7xt3Jh90TYsY44l8nTQ56mVWgrrGHxALiKi3EWFuIXHu7bQEVEROSM2CyW6j3V2lJLRETqQbNNqi0Wg5YxLQCwVhZxbedrPef8oqJw5uVhP3hQSbWIiEgjZbMahKGeahERqV/Ndk41AAFVCXNFUbXiY/OqNQRcRESksbJaLIRWG/6tnmoREfG+ZttTDfBtjoveQHFhHll5u9iSs4V2Ye2IT2hF+fffa7EyERGRRmziRYnEBIbBjqoC9VSLiEg9aNY91av2ulcBLS3K45M9n/DUmqf470//1QrgIiIiTcDQrrH0bmEcK9Dq3yIiUg+adVJt+ocBYFQUkRDiTqQzS47bVitTSbWIiEijdvwUL6eSahER8b5mnVQT6J5TbbEX0yq0FeBOqq2t3O/VUy0iItJ47ThUxKHDh48VqKdaRETqQbNOqi1B7qTaZj+up7o4U8O/RUREmoC5qzPYmrH/WIGSahERqQfNOqn2q+qptjmKiQ9x709d7iynJCYYAOfhHFyVWilURESkMbJZjZ+t/q2FykRExPuadVJtC4kEIMBZgr/hR8uglgAcspZiBAYC4MjUtloiIiKNkdViIYzSYwVOPSgXERHva9ZJtTUsFrvphwUXFB/yzKs+WKoh4CIiIo2dzc8gzDguqVZPtYiI1INmvU/1iF6tca2Jg5KDUHCA+/vfjwsX3aO7U5CQQOVPP2E/qJ5qERGRxsjqZyGU44d/q6daRES8r1kn1fERgRDdzp1UF+7ngp7Xes6VagVwERGRRs1mgVCO651WT7WIiNSDZj38G4Bw9zBvCqsnz7bWGv4tIiLSmIWYZVgM81iB5lSLiEg9aNY91fmllewtDqUPQMEB8srzSNuXRqWzklFH51RroTIREZFG6ZJ2AbD2uAL1VIuISD1o1kl1QZmdRbugjw0o3E9eeR5PrnmSYGswV3Z+FVBPtYiISGPVI/pnBZpTLSIi9aBZD/+ODvEn04wBwJm/n7bhbbFarJQ6SsmLcj9vcGRmYrpcvgxTREREzkRFoftfvwD3v+qpFhGRetCsk+qwQBsF/u69qc2C/dgsNhLDEwH4yT8fLBZMux1HTo7vghQREZEzUpCfC4A9yP0AHZcd9KBcRES8rFkn1QBGeBsA/EqywWmnU2QnAHYVZWCNiwPAoSHgIiIijc4PGfsA2F8ZcqzQWeGjaEREpKlq9kl1cFQ8laYfBiYUZdExsiMAP+b/iC1BK4CLiIg0VoGuEgAKLZHHCh1KqkVExLuafVLdKiqYLLNqJZPCA8d6qvN3HUuqtQK4iIhIoxPgdCfVBUY4YLgLlVSLiIiXNfukOiEyiEyq5loV7Pck1T/l/4S1VTwA9gPqqRYREWlsAhzFABQTDNZAd6EWKxMRES9r1ltqAYztk0Dw3q7w0zYoPEjbsLa8fvnrdIrshO0/aQDYDxzwaYwiIiJSd/6epDoIrP7gKAOnttUSERHvavY91W2jg4lpleQ+KDyAn8WPi1tfTHxIPAEdOgBQvn27DyMUERHxjhkzZjBw4EDCwsKIjY3lmmuuYXst2riVK1dy/vnnExgYSIcOHXjjjTdOqLNw4UJ69OhBQEAAPXr0YPHixfXxFerEv2r4d5F6qkVEpB41+6QagAj3CuAU7K9WHNSrF1gsOLKysB865IPAREREvGflypVMmjSJdevW8emnn+JwOBgxYgQlJSWnvCYjI4MxY8ZwySWXsGnTJh577DHuu+8+Fi5c6Kmzdu1axo0bx6233srmzZu59dZbufHGG/nqq68a4mudktVeBEChGXzcXtXqqRYREe9q9sO/AVZm2UgGHPn7seJe+fujjI+ICohiSNeuVGzdStnmzdhGjPB1qCIiImds2bJl1Y7nzZtHbGws33zzDZdeeulJr3njjTdo164ds2fPBqB79+5s2LCBWbNmcf311wMwe/Zshg8fztSpUwGYOnUqK1euZPbs2cyfP7/+vtAplNpLySk9TIDLnVQP6tYe9h1NqtVTLSIi3qWeaiD1OzsAZlVP9d7Cvfx1y19ZsmsJQX36AFC2ebPP4hMREakPBQUFAERHR5+yztq1axnxs4fKI0eOZMOGDdjt9tPWWbNmjZcjrp2VvxpDweBRrP/xMACDe3QAa1VSrX2qRUTEy5RUA5ZI9/BvW1kOOCo9K4BnFGQQ0Kc3AOWbt/gsPhEREW8zTZMpU6Zw8cUX06tXr1PWy8rKIi4urlpZXFwcDoeDnJyc09bJyso65X0rKiooLCys9vIWq80fqwuc+VVDvQPDjyXV2lJLRES8TEk1EBYVR4Vpcx8UHaR1WGsC/QKpdFWS37ElAGXffYfpcPgwShEREe+ZPHkyW7ZsqdXwbMMwqh2bpnlC+cnq/LzseDNmzCAiIsLzatu2bV3CPy1nrLvn3Sh0ArAjn+PmVCupFhER71JSDSREBZNpVg19KziAxbCQFOFeETwjogJLWBhmeTkVO3b4MEoRERHvuPfee1m6dCkrVqygTZs2p60bHx9/Qo9zdnY2VquVmJiY09b5ee/18aZOnUpBQYHntW/fvjP8NicL2v1A3FroTv4nLfxRPdUiIlJvlFQDCZFBZJruPwwodO9JfXQI+K7Cnwjq7R4CXrZFQ8BFRKTxMk2TyZMns2jRIj7//HOSkpJqvGbw4MF8+umn1cqWL1/OgAEDsNlsp60zZMiQU943ICCA8PDwai9v8U9IACDIvU01ea7AY1tqaU61iIh4mZJqoHVkEAc52lPtXqysY2RHwL0SeFC/vgCUpWuxMhERabwmTZrEu+++y/vvv09YWBhZWVlkZWVRVlbmqTN16lTGjx/vOf7Nb37Dnj17mDJlClu3buWtt95i7ty5PPTQQ546999/P8uXL+e5555j27ZtPPfcc3z22Wf87ne/a8iv5xHY2j2UPLTIPfy80BkAVn/3SfVUi4iIlymp5uc91QcB6BzVGYA9RXsI6luVVGsFcBERacRef/11CgoKGDp0KK1atfK8FixY4KmTmZnJ3r17PcdJSUl89NFHpKWl0a9fP/74xz8yZ84cz3ZaAEOGDOEf//gH8+bNo0+fPqSmprJgwQIuvPDCBv1+R4W2dffAh5YaFNqDqHQZmBr+LSIi9aTO+1SvWrWK559/nm+++YbMzEwWL17MNddcc9prKioqePrpp3n33XfJysqiTZs2PP7449x+++1nGrdXJbYIZuwlA2DtB57h3wPiBvDhtR/SOrQ1ZoF7RdLKjAycBQX4RUT4MlwREZEzcnSBsdNJTU09oSw5OZmNGzee9robbriBG2644UxD86r4Vp3J8vfDVumkoCwY/MFlCcAPtE+1iIh4XZ17qktKSujbty+vvvpqra+58cYb+d///sfcuXPZvn078+fPp1u3bnX96HoTYPWjfWIX90HV8O9gWzDtwtvhZ/HDGhWFrX07AMq2fOurMEVERKQWYkNiCYlzL1ZWWuLuoXb5VQ3/dlb6KiwREWmi6txTPXr0aEaPHl3r+suWLWPlypX89NNPREe75y0nJibW9WPrX0Rr979VPdU/F9SnL/Y9eynbvJnQSy5uwMBERESkrmwtIqjcl0VZiT9Egcs4OqdaPdUiIuJd9T6neunSpQwYMICZM2fSunVrunTpwkMPPVRtUZSfq6iooLCwsNqrvq0+VDXXqvQI2N0N7sp9K3lk1SP8a8e/js2r3qJ51SIiIue8SHe7HmAG8rvLO2PxD3KXa061iIh4Wb0n1T/99BNffPEF3333HYsXL2b27Nn8+9//ZtKkSae8ZsaMGURERHhebdu2re8wWf5TBWVm1VPsqt7q3YW7+TjjY9YeXHvcYmVbajUnTURERHxnobELALvT4HeXd8HmX7WllpJqERHxsnpPql0uF4Zh8N5773HBBRcwZswYXnzxRVJTU0/ZWz116lQKCgo8r3379tV3mCREBXPwZ3tVH10BfOuRrQR27YLh74+roIDK3bvrPR4RERE5c/YI94NyZ4HTXaDVv0VEpJ7Ue1LdqlUrWrduTcRxK2Z3794d0zTZv3//Sa8JCAggPDy82qu+tY4KItM8ule1O6nu1aIXAPuL95PnKiawZ08Ayrdsqfd4RERE5Mw5I23uNwWVbM8qwm5UHTuVVIuIiHfVe1J90UUXcfDgQYqLiz1lO3bswGKx0KZNm/r++FprHRl43F7V7mQ/3D+cxPBEAL7L+Y6gPn0AKN24yRchioiISC0Z4X4AWPMrGfVSGrkVVX/yqKdaRES8rM5JdXFxMenp6aSnpwOQkZFBeno6e/fuBdxDt8ePH++pf8sttxATE8PEiRP54YcfWLVqFQ8//DC33347QUFB3vkWXpAQGUQm7p5qV8FBT3mflu5EesvhLQQPHgRAcVoapsvV8EGKiIhIrQSEWnAZYHVBZEXxsZ5qJdUiIuJldU6qN2zYQP/+/enfvz8AU6ZMoX///jz55JMAZGZmehJsgNDQUD799FPy8/MZMGAAv/zlLxk7dixz5szx0lfwjtiwQA7RAoDK3GPx927RG4Bvc74lZPBgLCEhOA4dovxb7VctIiJyroowneSGud/Hlebh0JZaIiJST+q8T/XQoUNPu/p1amrqCWXdunXj008/retHNSg/i0F5UDzYwZV/bK5375a9sRpWnKYTS0AAocmXUvjRxxR99plnRXARERE5t0Ta7RwOhxaF0LIsD4dRtW6Ks9K3gYmISJNT73OqG5NJ118OQFDxXqga3t0tqhtrb1nLmyPeBCBs+HAACpcv19ZaIiIi56jOlRUEBzkAiC3No9Ko6kdQT7WIiHiZkurjdOjcGyw2DHspFLi38fKz+BFoDfTUCbnkUgx/f+x79lKxc6evQhUREZHT6F1awnlW99adsaX52D3Dv9VTLSIi3qWk+nh+Vmjh3puaw9tPOO0yXfiFhhBy0UUAFJ3jQ9pFRESarYoibCHuPapjy/KOW6hMPdUiIuJdSqqPs+NQEVsdCe6Dw1s95T8c+YGb/nsT4z92r2oedrl7mHjRZ/9r8BhFRESkBi4nZmUR9tCqqVyWUmIjI9znNKdaRES8TEn1cQ7kl/HxoUj3wXE91REBEXx/5Hu+z/meckc5oZelgJ8fFVu3Urlvn2+CFRERkZOrKMIJ3NapJQBRRUdoFxvlPqeeahER8TIl1cdpFx3MTrM1AGb2Nk95QkgC0YHROEwH23K3YY2KInjgQACKPv3MJ7GKiIjIKVQUYQUqqnqqzaIinBXuoeCaUy0iIt6mpPo47aOD2W20BcA8vA2qVvc2DIM+LfoAsOXwFgDChh8dAq6kWkRE5JxSUQhAoM2guGqt0YIDR9xv1FMtIiJepqT6OFY/C34tOmA3/bDYS6Cg+n7VAN/lfAccm1ddtmkT9uzshg9WRERETq7cnVRHYuFw1VTq9Rsz3G9cds+2mSIiIt6gpPpnOraKJsOMdx8cN6+6dwt3Ur0lx91TbYuLI7BvHzBNij//vMHjFBERkVOoKAIg0rCSE264y3Jyj513VvggKBERaaqUVP9Ml7gwz7xqDh+bV92rRS8MDA4UHyC33N0we1YBV1ItIiJy7qga/h1pCSAn3F1kyT0uqdYQcBER8SIl1T/TOTaUXWYb98FxSXWYfxiDWg1iePvhlNhL3GUpKQCUrvsKV2lpg8cqIiIiJ1FeAECkXyA5Ee6ealvOYaCq11qLlYmIiBcpqf6Zizq14FdjR7oPjkuqAf464q+8OPRF2oa5FzPz79gRW5s2mJWVlKxZ09ChioiIyMlUDf8+L6AlZnQvAAJyD4O1atUy9VSLiIgXKan+mZAAKzGJ7pW+ObzdswL4yRiGQWhVb3XRihUNEZ6IiIjUpGr49+WhScTF3gRAQG42WAPc553qqRYREe9RUn0yMR3B8HM3ykWZ1U6ZpsnB4oOe47CUoQAUr1yFqdVERUREfK9q9W8CwqiIaQlAYEEepqUqqVZPtYiIeJGS6pNYti2PLGuC++C4IeB2p51LFlzCyIUjySnLASB4wAAsoaE4c3Io//ZbX4QrIiIix6sa/m0GhNGxWwucflYM04W93N99XnOqRUTEi5RUn0RGTgmbyqu21co+llTb/GxEBUQBsD3Xvd2W4e9PyMUXA1CUltagcYqIiMhJVA3/3kEl07/7BUci3FO57CU293n1VIuIiBcpqT6JrvGhJ91WC6B7dHcAtuZu9ZR5hoCvSGuA6EREROS0bMEQHENkSBwAe1q4k+qKvKo/e7RPtYiIeJGS6pPoHBvGLpd7Wy3Xz5LqrtFdgWM91QAhl14KFgsV27ZhP3gQERER8aEb5sIjPxHZx71I2U/u3JrynKrFRx1KqkVExHuUVJ9E68gg9lrbAWBmb6u2Ani36G4AbMs9lmxbo6II6t8f0BBwERGRc0WAXwA2I5CfqmZ0lWc73G+UVIuIiBcpqT4Ji8XAGtsFp2ngV1EAxYc85472VO8p3EOpvdRTriHgIiIi555AvzAy4g0AKo7YcTkMJdUiIuJVSqpPISkumj1m1Xix44aAtwhqQcuglpiY7Mjb4Sk/ul916bp1uEpKGjRWERERObkgvzDyQw2KQkLAhPJ8q+ZUi4iIVympPoUucWH8SFv3weHt1c5d2/labu91O1GBUZ4y/w4dsLVrh2m3U7xmTUOGKiIiIqcQYg0HYH98CwDK82zqqRYREa9SUn0Ktw5uz7BLLnEf/Gyxsnv738sD5z9A+/D2njLDMAgdmgxA8cqVDRaniIiInFqXsAupzLuQrFj3WinluTZtqSUiIl6lpPoUAm1+WOLc22dx6PtaXROa7E6qS1auwjxucTMRERHxjUvirqUi61oOx/YDoDzPXz3VIiLiVUqqTyfBvaI3mVvAaa92Krc8lzUH1+BwOTxlwQMHYgQH4zh8mIqtWxERERHfsvm5/9TZH+Puqa4osOIqKz3dJSIiInWipPo05qS7KDZCwFEG2T94yl2mizGLxnD3p3ezu2C3p9zi70/I4MGAhoCLiIicC1pHBjK2fwRd+sfiF+IPpkHF/mxfhyUiIk2IkurTOFhQwUZHB/fBgW885RbDQtco99ZaW3Or90iHJl8KQPHKVQ0TpIiIiJzSfvsa0sp/yzbXXwhsFw1A+e7DPo5KRESaEiXVp9E5LozNZkf3wf5vqp07ul/19tzqK4OHXupOqss2b8aRl1f/QYqIiMgpRQZEApBfkU9g+5YAlO/N9WFEIiLS1CipPo3urcJId1Ul1QeqJ9XdorsBsC2v+srgtvh4Arp1A9OkZPXqBolTRERETi7CPxKAvPJ8AhPjACjfX+DDiEREpKlRUn0afdpE8q3ZCQDz8DYoL/Sc8yTVudtOWOn7aG+1hoCLiIj41v4jBgCHS/MI7NAGgIqsYky7/XSXiYiI1JqS6tMIDbASHdeG/WYLDEzITPec6xjZEathpaCigEOlh6pfd3S/6i++wHQ4EBEROVesWrWKsWPHkpCQgGEYLFmy5LT1J0yYgGEYJ7x69uzpqZOamnrSOuXlvt8POiow0v3GsOOIi8Bic2E6TSp27fJpXCIi0nQoqa5B/3aRx4aA79/gKQ/wCyApMglw91YfL6hvX/wiInAVFFC2eXODxSoiIlKTkpIS+vbty6uvvlqr+i+//DKZmZme1759+4iOjuYXv/hFtXrh4eHV6mVmZhIYGFgfX6FOwvxDMF1+ABQYJoFR7h7q8h9+ON1lIiIitWb1dQDnuv7toti3rQdUfnXCvOqJPSdid9k9Q8GPMvz8CLn4Ygo//JDilasIPv/8hgxZRETklEaPHs3o0aNrXT8iIoKIiAjP8ZIlS8jLy2PixInV6hmGQXx8vNfi9BZ/qwXTGYxhKSLfdBAdZac0O4Dy73+A66/3dXgiItIEqKe6Br84vw2//eWN7oOfJdVjO47lus7XER9y4h8RniHg2q9aRESakLlz53L55ZfTvn37auXFxcW0b9+eNm3acOWVV7Jp0yYfRVid1WLBWdoRM38oLYNaEBitnmoREfEu9VTXwDAMaNUXDD8oyoTCgxCecEI90zTddauEXHwxGAYV27djP3AAW+vWDRm2iIiI12VmZvLxxx/z/vvvVyvv1q0bqamp9O7dm8LCQl5++WUuuugiNm/eTOfOnU96r4qKCioqKjzHhYWFJ613tmxWC+UHbyLAaqFFkJWKqEoAyrdtw3Q4MKz6U0hERM6Oeqprwz8EM7a7+/1x86oB7C477219j2s/uJaCimNbdFijoggeMACAQ7NmNVioIiIi9SU1NZXIyEiuueaaauWDBg3iV7/6FX379uWSSy7hn//8J126dOGVV1455b1mzJjhGVoeERFB27Zt6yVmm8X9wNvudIE1AP8wJ4YVzPJy7AcO1MtniohI86Kkuhb+8fVe/pXl3tvy50PA/Qw//r3j3/xY8CPvb63+5D720d+Dnx9FHy+jcPnyhgpXRETE60zT5K233uLWW2/F39//tHUtFgsDBw5k586dp6wzdepUCgoKPK99+/Z5O2QAgvz9GN0rnlG9Y1idv4PDVj+sQe5zjtzcevlMERFpXpRU10KL0AA2ODq4D36WVFsMC3f3vRuAd7a+Q3FlsedcUM+exNx5JwBZT/8RZ35+g8QrIiLibStXrmTXrl3ccccdNdY1TZP09HRatWp1yjoBAQGEh4dXe9WHsEAbr//qfJwt53HPlpf5NCQIv6pFyZ15+fXymSIi0rwoqa6Ffu0iSXd1AsA8uBFczmrnh7cbTlJEEkWVRczfNr/auRb3/Bb/jh1x5uRwaMaMBotZRETkZIqLi0lPTyc9PR2AjIwM0tPT2bt3L+DuQR4/fvwJ182dO5cLL7yQXr16nXBu+vTpfPLJJ/z000+kp6dzxx13kJ6ezm9+85t6/S51cUH8BQCsDQrCL8Ddjjvz8nwZkoiINBF1TqpXrVrF2LFjSUhIwDAMlixZUutrv/zyS6xWK/369avrx/pUi9AAKiI7UmwGYlSWQM6Oauf9LH78us+vAXj7h7cptZd6zlkCAkh49k9gsVDwwVKK0tIaMnQREZFqNmzYQP/+/enfvz8AU6ZMoX///jz55JOAezGyown2UQUFBSxcuPCUvdT5+fn8+te/pnv37owYMYIDBw6watUqLrjggvr9MrVkmiaD4gcD8HVgABb/qqQ6X0m1iIicvTon1SUlJfTt25dXX321TtcVFBQwfvx4hg0bVtePPCf0bdeCb11VQ8B/tlgZwKjEUbQPb09+RT7/2vGvaueC+vYl+rbbAMh6ahrO4uITrhcREWkIQ4cOxTTNE16pqamAezGytJ89AI6IiKC0tJS77rrrpPd86aWX2LNnDxUVFWRnZ/PJJ58wePDgev4mtdfp8Y+54oUfifKPoMxi4UiQCainWkREvKPOSfXo0aN55plnuO666+p03d13380tt9xyTjWyddGvbSSbzY7ug5/NqwawWqxM6DkBgPe3vo/zZ0PEW953L7b27XAcOsThOXPqO1wRERGp4mcYgIXzYs4HYHeIe0Vwh5JqERHxggaZUz1v3jx+/PFHnnrqqYb4uHrRv10kW1xJAJiZm09aZ2zHsVzX+TpmJc/Cz+JX7ZwlKIj4qqF1ee++R/kPP9RvwCIiIgKA1c+dRPePuRCAbeHuvamd+QWnvEZERKS26j2p3rlzJ48++ijvvfceVqu1VtdUVFRQWFhY7eVrPRLCCUty7zvNoe/BaT+hToBfANOHTKd3y94nvUfoRRcRPmY0uFxkTp+O6XLVZ8giIiIC2Pzcf+70rEqqdxxNqtVTLSIiXlCvSbXT6eSWW25h+vTpdOnSpdbXzZgxg4iICM+rbdu29Rhl7QRY/XjuzqsgIALDWQGHt9d4jWmaJ5TF/v5RLCEhlG/eQv4//3WSq0RERMSbbFU91aEBLXjmcC6PFh8BlFSLiIh31GtSXVRUxIYNG5g8eTJWqxWr1crTTz/N5s2bsVqtfP755ye9burUqRQUFHhe+/btq88wa88woFUf9/tTDAEHyCrJ4o9r/8jUL6aecM4WF0vL++8HIPvFF3EcOVIvoYqIiIib1eL+c8fhgqvLnbTzc482U1ItIiLeUK9JdXh4ON9++61nP8yje1Z27dqV9PR0LrzwwpNeFxAQQHh4eLXXuaI0pqf7zWmS6hJ7Cf/c8U8++ukj9hWe+EAg6pabCejRHVdhIdkzn6+vUEVERASwWd091ZVOF1gD8AtwT79yFhRgOp2nu1RERKRGdU6qi4uLPQkyQEZGBunp6Z49LadOncr48ePdN7dY6NWrV7VXbGwsgYGB9OrVi5CQEO99kwZwIL+MqWvdPzLXwfRT1usY2ZGLW1+Mick7W9854bxhtdJq2jQwDAo++IDy7TtOvImIiIh4xZAOLbi8eyzhgVawBrAsKtB9wjRxngPrtoiISONW56R6w4YN9O/fn/79+wMwZcoU+vfvz5NVK1tnZmZ6EuymJiEikINB7rnhZta3cJqFxm7r6d6XesmuJRRUnLi6aFCfPoSNGAHAkblv1kO0IiIiAvDcDX1487aBdIoNA2sAb0aHU1yVV2sIuIiInK06J9VDhw7FNM0TXqmpqQCkpqaSlpZ2yuunTZvm6eVubAzDIKpdD8pMf/wcpZD74ynrXhh/IZ2jOlPmKOOzPZ+dtE7Mr+8CoPDDj6jcv79eYhYREZHj+AUQ73BSFOQ+VFItIiJnq0H2qW5K+rWP4QezvfvgNPOqDcNgVOIoAD7fd/IF2YJ69iTkoovA6ST3rbe8HquIiIi4OZwu9ueVgjWQBIdDSbWIiHiNkuo66t82iu9cie6DzPTT1k1pmwLAuoPrKLWXnrROzK9/DUD+wkU4cnK8FaaIiIhU2ZVdRO9py7nq1S8xrf60cjgpCnYvXuZQUi0iImdJSXUd9W0bwQ9mEgAV+zadtm6nyE5c0voSxvccT6Wz8qR1gi8YSGDfPpgVFeT+/W2vxysiItLctY0OxuFykVtSSSU2WjkcFHp6qvN9GpuIiDR+SqrrKNjfSkl0LwAsWVvANE9Z1zAMXrv8Ne4/734iAyNPWadFVW913vz5OIuKvB6ziIhIcxZg9aNrfBgAhXa/qp5q9zkN/xYRkbOlpPoMjLwsGZdhxWYvhPw9Z32/0JQU/Dt1xFVcTN78f3ghQhERETlenzaRAORXWqrmVB8d/p3rw6hERKQpUFJ9Bsb2T8QS39N9kLmlxvrljnJW7lvJj/knXy3csFhocZd7JfDct97S/C4REREv69M6AoAjFRDrdPKrdhcC4DrJ8O+tmYVkF5Y3ZHgiItKIKak+U636uv89zQrgR/3f1//H5M8n868d/zplnfAxYwjo3Alnfj6HX3zRW1GKiIgIx3qqD5eBH9A6wn3syK/+IPunw8WMfnk1Fzz7v4YNUEREGi0l1Wdof2AXAEr3bqyxbnKbZABW7F2BeYo52IbNRvy0aQDk/+vflG6s+b4iIiJSO53jQgmwWih2WAHwC/IDTlyobMNujRYTEZG6UVJ9ht76KRwAIzP9tIuVAQxKGESgXyAHSw6yPW/7KesFn38+ETdcD0DWU9Mw7XavxSsiItKc2fws3DYkkR5tWwDwjXEYgIrcw9XqOY9r00/1IFxEROR4SqrPUHTSeThMC0GVuVCUddq6QdYghiQMAeDzvZ+ftm7sgw/iFxlJxc6d5L6tLbZERES85bEx3embGAfAt1Z3j7SluKzaQ2yn61giXeFwNWyAIiLSKCmpPkN9kuLZZbZ2H9RiXnVKuxQAVuxbcdp61qgoYh95BIDDr/4Z+4EDZxeoiIiIHGMNAKBFUBAu9wLgOAsKPKcvSIoGwM9iVEuwRURETkVJ9Rnq2zaS781EAMq2La+xfnKbZCyGhW252/gp/6fT1o249hqCBwzALCsj65k/eSNcERGRZs80TYoc7rnU8YaV4kB3+fF7VXeJC2P3/13Bj8+OISTA6oswRUSkkVFSfYYigmx8HXY5AAGb34bc0yfKUYFRngXLvjz45WnrGoZB/LSnwGajeMUKij77zDtBi4iINGOmCX/9cj8A4RUuCoPd5drKUkREzoaS6rPgSkphlbM3FpcdPptWY/0Hzn+Af439F7f2uLXGugGdOhFz++0AZD3zJ1wlJWcbroiISLNmsRhER7oXGg0stlMU5C4vOXxsbZSdh4r4Zk8emQVlOJyaUy0iIjVTUn0WhveI40+OX+LEAj98AHvXnbZ+UkQS3aK71fr+LX5zN7Y2bXBkZXH4lVfPNlwREZFmLy7KnVS7SsopD7EBkHdoj+f8nM93cf3raxg843PW/ZTrkxhFRKRxUVJ9FoZ2jeXRCddj9P+Vu+CTx2vcXuuog8UHOVh88LR1LEFBxP/hCQBy33mH8q1bzypeERGR5q5VTBQAZWWluCJCASg6fGxR0KLyYyuBl1Q6GjY4ERFplJRUnwV/q4WUrrFYLnsCbCFwYAN8v6jG6/69499csfgKZn8zu8a6ocnJhI0cCU4nmdOmYbo0FE1ERORMtW4ZCUBleRkXdhsBQFtXhOd8UfmxRLpUSbWIiNSCkmpvCIvDddH97vefTQN7+Wmr92rRC4fLwbLdy/gx/8cabx/32FQsISGUb97C/kmTOTIvlZKvv8ZZXOyF4EVERJqPlpFhANiwY4a696w+fqGyaj3VFc6GDU5ERBolJdVe8NYXGVy2pjdlgXGQvxfS3ztt/W7R3RjWbhgmJn/Z/Jca72+Li6Plg1MAKF6xguznnmPv+NvYceEgsmfNwrTba7iDiIiIABhW9+pkAVRSGuQe/u3My/ecLyw71jtdVqmkWkREaqak2gsKy+3sLjRZFHidu2Dj32u85rd9fwvAst3LyCjIqLF+9C230P7dd2h5/32EXj4Ma6tW4HRy5M257JkwEfuh7LP6DiIiIs2C1R+A9uF+mNEGAAf2H1uzRHOqRUSkrpRUe8E1/VoD8OKhvph+/pC52f06ja7RXUluk4yJyd+/rzkJBwgeMIAWv/0tbV99lc4rPqf1nJexhIZS9s03ZFx3HSVr1571dxEREWnSrIEAhNtcWGPc751Vw7+dLpOS43qnS9VTLSIitaCk2gsSW4RwXrtIjpjhZMQMdRdufKfG627v5d6HeumPSzlcerjOnxs+YgRJ//4XAV274jxyhL133EnxypV1vo+IiEiz4efuqcZRQWyrTgAElzqxu+y4TJMnruhO2+ggkru0pGPLEB8GKiIijYWSai+5tr+7t3pu2SXugi3/BHvZaa85L+48+rXsh9Vi5fsj35/R5/onJpK44B+EjxkDLpd7jrVTT9ZFREROqqqnurKyjGyiAQiqhEP5+7H5Wbjzkg6sfuQy/n77BYwb2M6XkYqISCOhpNpLruyTgM3P4P3DSdhD20BFAWz9T43XTb9oOp/e8ClD2w4948+2BAYSP+0pLBERVOzcReFHH5/xvURERJq0qjnVleVl/GtrAc6qv4QOHdzlw6BERKQxU1LtJVEh/iR3icXEwvqoMe7CjW/XeF2HiA5EBETUWK8mfuHhxEycCMDhV1/BdGhxFRERkRNU9VT7YyevzEF5iA2AnKwM8ksr2bg3j71HSgH3HGsREZGaKKn2ol8MaMOEIYlEDJkIGLB7NRypeR/qozZlb6LSWXnGnx9966/wi4rCvmcvBR98cMb3ERERabL8AgDwN5wUlJRjD3NvsZWfvZevM3K57rU1XPr8Cro88TE3vLHGl5GKiEgjoaTai0b2jGfaVT3p2b0HdBrmLtxU84JlAA+tfIjxH4/nPz/WPGT8VCwhIcT8+tcAHP7zn3FVnnmCLiIi0iRZAzxvS8tKMSPCACjPyaao3D3Ky89iUOlwUVqhNUpERKRmSqrry3nj3f+mvw/Omodi927RG4DU71Nxma4z/tiom2/C2rIljoOZ5P/732d8HxERkSbpuKS6pLSEVq27ADC2xVDPHtXx4e4h4tqnWkREakNJtZc5nC7W/niEtw53g+AWUHwIdiyr8bobutxAqC2U3YW7+eLAF2f8+ZbAQGJ++xsAjrz+BqUbNmCqx1pERMTNYsU03H/+lJWVERgTC7j3qi6s6qmOj3An1dqnWkREakNJtZflFFdy89/W8cdluyjpeZO78Jt5NV4XYgvh2s7XAvD+1vfPKobIG27AmtAKx+HD7PnVrWy/4EL23n47ef9YgOk6815wERGRRs8wwM+dNDsqSjEi3IuFOvPyPT3VceHu3uySCvVUi4hIzZRUe1l8RCB920ZimvBZ0Gh34a7/QW5Gjdfe3O1mDAy+PPglPxX8dMYxWPz9afeXvxA2ehR+0dGY5eWUrFlL1rRp5L373hnfV0REpEkIjgLg6WFxWCIjAVi79RMOlx4GID7cvXhZhcOlFcBFRKRGSqrrwciecQAs3O0PHS8DTPgmtcbr2oa19exXfba91QGdO9PmpZfo/OUXdPjPUqKrttvKnjWL8u07zureIiIijZkRlQTAZXGlBMREA1BxJIdD5e4H4PERxy1mpnnVIiJSAyXV9WBUz3gA1v6YQ0mfCe7CTe+Co6LGa3/V/VcAfHPoG5yus5/LZRgGAZ07E/vIw4QmJ2NWVnLwoYdwVdQci4iIND2rVq1i7NixJCQkYBgGS5YsOW39tLQ0DMM44bVt27Zq9RYuXEiPHj0ICAigR48eLF68uB6/xVmKau/+Ny8Dvyh3r3VYmUliqyLuu6wTFybFMKRjDJd1i0WzpkREpCZKqutBh5ahdI4Nxe40+czRD8JaQWkObK15u6yB8QN5/fLX+ffYf+Nn8fNaTIZh0OrZP+EXE0PFzp1kv/CC1+4tIiKNR0lJCX379uXVV1+t03Xbt28nMzPT8+rcubPn3Nq1axk3bhy33normzdv5tZbb+XGG2/kq6++8nb43hGVCMDBjG3k2YIBCC8Da1A2U0Z0pW/bSN6/axBvTRhIRLDNh4GKiEhjoKS6noys6q1etjUHzrvNXbih5gXLDMPg4tYXezWhPsoaE0PCjGcByHv7HYpXr/b6Z4iIyLlt9OjRPPPMM1x33XV1ui42Npb4+HjPy8/vWDs1e/Zshg8fztSpU+nWrRtTp05l2LBhzJ4928vRe0lVUr1n1w9synfPmQ4rhV15u3wYlIiINFZKquvJ0aR6x6EiXP3Hg+EHe76A7G01XHmM3WknqyTLq3GFXnopUb9yDzE/OPUxHLm5Xr2/iIg0Tf3796dVq1YMGzaMFStWVDu3du1aRowYUa1s5MiRrFmz5pT3q6iooLCwsNqrwVQl1W0t2RzxDwU/PwIcUPDTTnYcKsTu1JhvERGpPSXV9aRX63D+e+/FfDYlGUtka+hatRJ4LbbXAvg682tGLhzJwysfxjS9u/Jo7EMP4t+pI86cHLKeesrr9xcRkaajVatW/PWvf2XhwoUsWrSIrl27MmzYMFatWuWpk5WVRVxcXLXr4uLiyMo69YPhGTNmEBER4Xm1bdu23r7DCaqS6gSOUGB3EnTBQAD6bStj5KsfkFVQzq/e/Ipuf/iYFduzGy4uERFplJRU1xPDMOjVOgLDMNwFA9yrb5M+HypLa7w+MSKRwspC0g+nk7YvzauxWQIDaT1zJthsFH36GQVLPvDq/UVEpOno2rUrd911F+eddx6DBw/mtdde44orrmDWrFnV6nnauyqmaZ5QdrypU6dSUFDgee3bt69e4j+pkJbYLYFYDBOzYD/hl18OwIDtBhZrIeGBNuxOF+V2F6UVZ79oqIiING1KqhtAud1JUetLILI9VBTA9o9qvCY2OJZbe9wKwEsbX8Lh8u6WHoE9etBy0iQADj3zDPYDB7x6fxERaboGDRrEzp07Pcfx8fEn9EpnZ2ef0Ht9vICAAMLDw6u9GoxhUBzUGgD/or2EVSXVXQ+4CMuLITTQSkiAFYASbaklIiI1UFJdz95Zu5sL/vQZb325F/qMcxdu+Wetrr291+1EBkSSUZDBkl1LvB5bzJ13ENS/P66SEg4+OhVT+4aIiEgtbNq0iVatWnmOBw8ezKefflqtzvLlyxkyZEhDh1ZrZaHu4ebBJfuwxcVhdO+JBZOhOVvxsxgE+7sXYiutUFItIiKnV+ekuq77Wy5atIjhw4fTsmVLwsPDGTx4MJ988smZxtvohAfZKCx38M8N+3D2+oW7cNdnUJJT47Vh/mHc3eduAP6c/mdK7TUPG68Lw2ol4bn/wwgOpnT9eg7PmYOr1LufISIi55bi4mLS09NJT08HICMjg/T0dPbu3Qu4h2WPHz/eU3/27NksWbKEnTt38v333zN16lQWLlzI5MmTPXXuv/9+li9fznPPPce2bdt47rnn+Oyzz/jd737XkF+tThzh7r2qI8rdI7Ucgy8BYEjmdwCepLqkUsO/RUTk9OqcVNd1f8tVq1YxfPhwPvroI7755htSUlIYO3YsmzZtqnOwjdHInvFEBNk4kF/Gl/lRkHAemE74blGtrh/XdRxtQtuQU5bD2z+87fX4/Nu1I+7R3wNw5I2/sGPIRey/9z4K/vuhEmwRkSZow4YN9O/fn/79+wMwZcoU+vfvz5NPPglAZmamJ8EGqKys5KGHHqJPnz5ccsklfPHFF3z44YfVtuQaMmQI//jHP5g3bx59+vQhNTWVBQsWcOGFFzbsl6uD8AT3PttDoosByO5/HgDdM3+gsrCAYH/38O9SDf8WEZEaGOZZLP1sGAaLFy/mmmuuqdN1PXv2ZNy4cZ4GvCaFhYVERERQUFDQsHOuvGTa0u9JXbObK3q34s8dv4Zlv4fWA+Cu/9Xq+mUZy3h41cNc3fFqnrn4Ga/HZ5omuXPnkvePBdj37/eUWxNa0XrmTIIHDPD6Z4qINHaNvW061zT4z3P7Mpg/DuL7wG9W8+GWA/j/Zjhtck1sf/w9i8MH8ecVP3LToEgO+L/B6KTR3NL9lvqPS0REzhm1bZsafE61y+WiqKiI6OjoU9bx6d6V9WDcQPe8reU/ZJGbdIV7z+oDG+DIj7W6fkTiCBZcuaBeEmpwPxyJufNOOn66nKRFC4m5+26s8fE4DmayZ/xtZL/8MqbdXi+fLSIi4hNR7uHf5O8BoFNsON93jwKg4NPltI8J4YKkaOLNI1z4r638b+GLvopURETOcQ2eVL/wwguUlJRw4403nrKOT/eurAfdW4XTt00EdqfJoh126HiZ+0QtFyyzGBZ6xPSoxwjdDMMgsEcPYh/4HR3++18irrkGXC6OvP4Gu3/1Kyp37673GERERBpEZFVSXV5AWcERusaH4UruA4D/199xQ+9Y3h4azeiXX2TY2hImLyihdN8eHwYsIiLnqgZNqufPn8+0adNYsGABsbGxp6zn070r68mNVb3V/1i/D7NP1QOFLQugjqPvs0qyeG/re94O7wR+oSEk/N8MWr/4ApawMMo3b+Gnq68h542/YFZW1vvni4iI1Cv/YA4TCUDO/u0ARPe7gNxQsJbbyX7hBXbffAuurEMABNph31NPcBaz5kREpIlqsKR6wYIF3HHHHfzzn//k8qr9IE/Fp3tX1pOr+ibwm+SO/PXW8zG6XQG2EMjLgAPf1Poe+eX5XLXkKv7v6/9jU3bDLPQWPmYMHT5YQsiQwZgVFRyePZuM66+ndGPzWGhORESarixLPACVh39if14pwX5tWN/FACDv7XcwS0s53LMVM++Mwu4HrjUbKPr4Y1+GLCIi56AGSarnz5/PhAkTeP/997niiisa4iPPOWGBNh4d3Y0OLUPBPwS6X+k+sWVBre8RGRjJmKQxALyw4YUGe1puS0ig7dy5JMx8Dr+oKCp27mLPLbdw8PHHsWdnN0gMIiIi3pZjc++1beZmMPuznfx+fjZfVyXVAJ/0N7jvimwquyexeLD7T6asPz2LMz/fF+GKiMg5qs5JdV33t5w/fz7jx4/nhRdeYNCgQWRlZZGVlUVBQYF3vkEjdaTDNe433/4bymv/s7in3z0EWYPYfHgzn+39rH6COwnDMIi46io6fPQhEVXbqBQsXMRPo0aT85e/4qqoaLBYREREvCE/oDUAlvw9FJXbMR0R7O4US9roViz7VX/mjvLD4uzOkNZDWDLYoKBVOM4jRzj0/PM+jlxERM4ldU6q67q/5V/+8hccDgeTJk2iVatWntf999/vpa/QuJRUOPjtu99w6UIXjsgkKMuF5U/U+vrY4FjG93A/tJj9zWzsroZdldsaFUXCs3+i/fz3CezTB1dpKYdfeomfRo+hfPv2Bo1FRETkbJQEu5PqgOJ9FJY5AINHer7DnS98wqKOBwEwigaRGJ6Iw2qw9AZ3/YKFiyheudJXYYuIyDmmzkn10KFDMU3zhFdqaioAqamppKWleeqnpaWdtn5zE+zvx5GSSkrsBnNCfwcYsPFt2FX7XueJvSYSHRjN3qK9/HN77VYQ97bg/v1J/Md8Ep6fiTUuDvvBgxx86CFcWsRMREQaiYqwdgAEl+6nqML9kDoiyJ9V+1aRX3kElyOUyoLu9Ijpwbiu4xg44lYib74JgH2/vYfsF1/S4p0iItLwW2o1d4ZhMG1sTywGzNnVksxut7lPLL2v1sPAQ2whTOo3CYA3Nr9BYaVv9vE2LBYixo4lacli/KKjqdi5iyN/+atPYhEREakrZ7g7qQ4vz6S0zD2NKTzIyr92/gsAe/4ASu2QGJ7IE4Oe4NrO1xL3yCOEXzXWveXkX/9Kxo3jNFJLRKSZU1LtAz0Swvnlhe79Me8+OAZXVBIUHoBPHq/1Pa7rfB3do7vziy6/wGpY6yvUWrFGRRH/B/cQ9py//IXy7Tt8Go+IiEhtnN+7J07Dhh9OAsuyAMipzODLA18CYM8fiNNlUuFwea6xBAXReuZMWs952b1457ZtZNzwC0rWrPHJdxAREd9TUu0jU4Z3ITrEny3ZDuaEPoCJAZvegZ21GwZutViZf8V87jvvPoJtwfUcbc3CRo0idNgwcDjIfPxxTIfD1yGJiIic1vmJMfhFJwIQXemeQ90tpiMXtb6ICT0nYtpjACitdFJiL+H7I99zoPgAAOEjRtDhP0sJGTIE7Hby5v/DJ99BRER8T0m1j0SF+PPnW87DajGYvbMFW1q752jxn/ugvHbDuf0sfp73DbW91qkYhkH8k09iCQuj/LvvyP372z6NR0REpFaiEgEY381kwpBEYkPDeOPyN3hwwBT6tY1kYGIUTpfJ8+uf56b/3sSinYs8l1pbtKDFvZMBKF2/HtPlOtkniIhIE6ek2ocGd4xh+tU9iQsPwDLsSTg6DPyzaXW6T3p2Ord+fCubD2+un0BryRYXS9zvHwHg8Jw5FH70kf7AEBGRc1a53Um2NR6AEQkVTLuqJ0H+xx5YL5l0Ef/6zRBahgWQFJEEwJ7CPdXuEdSrF0ZQEM78fCp27mq44EVE5JyhpNrHfnlhez6dkkzvDgkw9mV34Ya5sKf2c7MW7lzI5sObmbV+ls97rCOuv56Qiy7CrKjgwJQHybj2Oor+9z9Mp5Oyb7/l8GuvsfuWX7L39jtwFhf7NFYREWneDuaX8ddvqx7+5u0+bd324e61UHYXVK9n2GwEn3ceAKVff+3tEEVEpBFQUn0OCA+0ud90SCa3a9Uw8A8mg72sVtff2/9egqxBpB9O56olV/HUmqdYvHMx+4v211PEp2YYBm1emUOLeydjCQ2lYvt29k+azPbzB7D7FzeSM+cVyjZupGTNGrJfeKHB4xMRETkqMtiffWYsAGXZP3KkuOKUdRPDEwHYW7QXl1l9FFbwBRcASqpFRJorJdXnkE9/OMSI74eTa4mG3B9h5XO1ui42OJYHz38Qi2Fhd+FuFu1cxJNrnuTKxVfy2Z7a73/tLZbgYFpOmkSnzz4l5u67MYKDMcvLsYSGEjZiBC3uuQeA/Pn/oER/gIiIiI+EB1rZW5VUlx76kVvnVm+TJr23kYF/+oz/bT1E67DWWA0rZY4yskuzq9ULvmCg+x4bNmjak4hIM+TbvZikmqQWwZT7hfFo+W381f8l+HIO9LgGEvrVeO24buMYkTiCLYe3sCl7Eyv3ryTQL5BeLXrVe9yn4hcZSewDvyNm4gTsmZkEdOqEYXP3yjsOHyb/X/8i8w9/oMOSJViCgnwWp4iINE9WPwu5AQkAxBhFJNiqT0sqKLNzuKiCwnI7NouNNmFt2F24m4yCDOJD4j31PPOq8/Ko2LWLwC5dGvR7iIiIb6mn+hzSKTaMOTf341NzIP91XgimE+aNho8eqXGuF0BUYBTJbZP53fm/499j/03q6NRqjb6v+EVGEti9uyehBoh95GGscXHY9+zl8Kuv+jA6ERFpzvyDw9nlcifWvY0fq50L9vcjgRyGfzIcVjzrGQL+88XKqs+rXl//QYuIyDlFSfU55rJucfx+VDeesk9gs6sj2Evh67/AnP7wr4mQt6fmm+DebivAL8BzvD5rPU6Xs77CrjO/sDDipz0FQO68VEo3bsJ+4AClGzdR+MlyKnbu9HGEIiLSHEQF+5NudgKgu2N7tXMhAVbG+H1FaPlB2DyfqzpdxUMDHuL8uPNPuE/wwKoh4JrWJCLS7Gj49zno7ks7sC2zkKvTn+bqsJ282HYlfj+tgO8XwaHv4e6VYKv9cOk3Nr/Bn9P/zB297uB35/+u/gKvo7CUFMKvvJLC//6XPbfccsL5kCGDiZ4wgZCLL8awWDBdLuz791O5Zy/B5/XHEhLig6hFRKQpiQiyke7qyA1+q+hYubXauWB/PwZaqhLt/H0MT7j4lO2vZ7Gyqv2qDYv6LUREmgv9H/8cZBgGM67rQ6uIID4o6sL7nWfDb76A0DjI2Q6fP1On+7ULawfA3O/m+mThstOJe/wxrPHuIeqGzYatdWsCe/UCPz9K1qxl36/v5qcrx5IxbhzbBwzkxxEj2XfXXeyZeDuu0lIfRy8iIo3dTQPbscnl7qluXboVjltoLMTfjwFHk2pMyP3plPcJ6tWz2rxqERFpPpRUn6OC/P14/IruTBvbg3ED20F8b7iqau7x2j/D7i9qfa8xHcYwvsd4AJ748gkyCjLqI+QzYo2KouOyj+m85ku6btlMp/99RtK//0Wn5Z8QPXEiltBQKn/6ifLNWzBLSzH8/TECAynfsoUDDz6E6XD4+iuIiEgjdkWfVnTrO4gy059AZzEcOZYQt3buI8Yo8hybh3ewPXc7n+z+hEpnZbX7GP7+BPfvD2hetYhIc6Ok+hx2ZZ8EJlyUhL+16tfUZQScdxtgwpLfQnlhre/1u/N/x3mx51FiL2FK2hRK7edOL68lMBBrdDSGYXjKbK1bE/f7R+iUtoJW/zeD1i+9SIcP/0vXjd/Q7q25GP7+FK9YQdaf/oRpmj6MXkREGrtLurYiO7S7++DABk95T/v31Sse2cmEZRN4aOVD7C3ce8J9tF+1iEjzpKS6kbA7XRSU2WHknyCyPeTvhU8eq/X1NouNWcmzaBHUgl35u5i+dnqjSEb9QkOJvOYawkePJqBjRwyrleDzziPh+efBMMif/w+OvPmmr8MUEZFGKq+kkrbRQYR0uNBdsP9YUj3AqBr67R8GgHHkR88K4LsLd59wr2rzqhtBGysiIt6hpLoRWPfTEUa8tIqn//MDBITBNa8DBmx6B7Z9WOv7tAxuyazkWfgZfnyU8REbDm2o+aJzVPjIEcRNnQrA4Rde5PBrr2FWVtZwlYiISHWfb8vm+tfXMv9grLvguJ5q9q5x/9v7Bve/R3bSPqI9cPKk+vh51ZWaVy0i0mwoqW4Egmx+ZOSUsGjTfrZmFkLiRTB4kvvkorvh8PbT3+A458edz+8v+D0zLpnBwPiB9RRxw4gefyvREycCkDPnFTJu+AVlmzf7OCoREWlMokJsAHxW2NZdcOh7qCyFggPuUWGGBfpV7VCRs4v2Ye6k+qf8ExctO35edeEny+s/eBEROScoqW4E+raN5IrerTBNeGF5VQI97ClofxFUFsH746A0t9b3u7nbzVzZ4cp6irZhxT7yMAkzn8MvKoqKHTvYfdPNZE6fTsF//kvxF19S9v33OI4c8XWYIiJyjooI8gdgc1EojuBYcDkgczPsXQvAdiOJCcvKAAMqCuhblVSvObgGp8t54v2uvw6A3Hnz1P6IiDQTSqobiSkjumAY8NnWbHdvtdUfbnwbIttBXgb8awI4674SdkFFAfsK93k/4AZiGAYRV11Fh48+JOLqq8A0yZ//Dw4+/DD77ryT3dffwM6LLmb3TTeT+/bb2A9l+zpkERE5h0QE2areGThbned+e2AD7HEP/f6isgs/5jkh0t2TPdAIIcwWxpHyI2zJ2XLC/cJHjyawZ09cJSXkvPZ6Q3wFERHxMSXVjUTHlqGM6d0KgD+vqJqnFdICbpoPthDIWFmnhcsA1hxYw5hFY3jiyyca/YIq1qgoEp57jrZz3yT8iisIHjSIgK5dscbGgmFQlp7OoWdnsGvoUPbecSeV+/f7OmQRETkHhAdZPe8t7dwLjbF/A+xdB8B6V1dKK5wQ0xkAW14Gl7a9FIAVe1eccD/DYiH24YcAyFuwgMo9e+ozfBEROQcoqW5EJg3tBMCH32by0+Fid2F8L7juL+73X/8Fvv5bre/XIbIDlc5KNmZvZMW+E/8waIxCL7qI1i/Mon3qPDp8sITOq1bSKS2NuMceI6hfPzBNSr78kozrrqfo8899Ha6IiPhYbFggj4zqyhNXdMfWrmqtkd1fQPYPAGxwdaWk0gEt3Ek1OTu5tfutvHrZq0zqP+mk9wwZNIiQSy4Bh4Ps2bMb4FuIiIgvKaluRHokhDOsWyymCR99m3nsRPexcNkT7vcfPQxb/1Or+8WHxHNrj1sBeOmbl3C46j58vDGwxcUSPf5WEv8xn47LPiawbx9chYXsv2cSh55/HtNu93WIIiLiQ/cM7cSdl3SAhP6AAaU5gIkzqiM5RFBud+GKdj/Y5sguerboSXLbZAL8Ak55z9iHHgTDoOjjZZRtOXGYuIiINB1KqhuZh0Z2Zf5dg5iU0qn6iUsegvMnACb8+w7Ys7ZW95vYayJRAVHsLtzNe1vfw+5s2gmmf2Iiie+8Q9R498OE3LlvsWf8bVTu3u3bwERExPcCwiC2u+fQbDfY8748ooP7Tc7OWt0qsGtXIq6+GoDs52c1+mlWIiJyakqqG5nurcIZ3DEGwzCqnzAMGPMCdB0DzgqYPw6yt9Z4vzD/MO7uezcAszbMYuB7A0nbl+b9wM8hhr8/8Y89RuuXX8YSGkrZpk38dPU1HHlrHqbzxJVcRUSkGWl9vuetX+Jg/Czu9rYkLMldmLcbHJXklufy8saX+d3/s3fXYVZUbwDHvze3u3tZcunuVroEpFHpUlAxkJ9doCAIgoACgiLdKSHdvSANy3Z3796a3x+DF9ZdeunzeZ592J05c+bMADv3zDnnfXe/c9uq3N4eg0KrJff4cTI33tssMkEQBOHZIzrVz7CMXD3pubqbG1Rq6D4f/OpBfgb82R0y7h6Qq2e5nnQK6oSNxgajZMTTxtO8L1OXiUkyPYrmP3H2bVpTat06bBo2QCooIHHSJML79iX/8pUn3TRBEAThSfGtbf5WEdCQsu62VPC0Q2ftKQcGlYyQFo5KoWLhuYXsjNxJZGZksVVpvLxwGT4MgLgvvqTgethjuQRBEATh8RKd6mfUkqORNPxuJ91nH+JE+C05qrXW0GcZuJaDzBhY1BVy7pwnU6PSMKHJBA73OczOHjsp7VgagFx9LsO2D+ODvR+Qb8h/lJfzxGh9ffCbPx/Pr79CaWtL/pmzhHXpQsTAgWTu2IFkkNeZS5KEPj6evDNnMOl0d6lVEARBeGYFNgGFCpyDwKkUW99pytZ3muLjZA0u8vORlKs4WDhQ21PugO+M3Hnb6lxHjMC6Xj2k3Fxi3nkHU/7z+TwVBEF4kYlO9TOqgpcdapWS0KQcXp1zmLErQkjKKpB3WjtD/zVg7wPJV2Dxq1CQddc6FQoF7tbuaJRyzs6zyWe5nHaZ7RHbGbx9MCl5d+6cP6sUCgVOPXoQtGkjdm3agFJJ7uEjxIwew7VWrQnr1p3LtWpzrXkLwnv1JqxrN/Tx8U+62YIgCMKj4FIaBm+Xn6P/XWr1bwTwFDm15cv+LwPwd+Tft61OoVLh88NkVK6uFFy5QsK33z6SZguCIAhPjuhUP6Nq+jux+/3m9Knrh0IBa07F0PKHPRwLuzFq7egHr60FK2eIPQXL+4Oh4L7OUd+rPr+2+hV7rT1nk87Sc2NPjscffwRX83TQeHriO30aZXZsx2XoUFROThji4si/cAEpNxdUKhRWVuhCQwnv21dM4xMEQXhe+dYG51JFt7vcTKsF0NK/JQoUnE06S2Ju4m2rU7u54TN5EigUpK9cRcaGDY+i1YIgCMITIjrVzzBnGy0Tu1Vl7ahGVPFxIKvAwOilp0jJvtF5disP/VbJa8Cu74E1w8B0f+uj63jW4c/2f1LKoRSJeYkM2T6E2SGzMZqe34BeGh8f3N8bS5k9u/GZ8RO+M2cQtGUzFU6fovSmjWgDAzHExhHRrx95//wDgCE5mewDB0lftw5Twf29vBAEQRCeXuPX/EPLKXvYeTGhyEi1m7UbVd2qArAh9M4dZZsGDXB9U85rHffFlxRcu/boGi0IgiA8VqJT/Ryo7ufI8uH1Ke1mQ0JmAZvO3pLD2rcW9F4MSg1cWAfnVt93/aUcSrGswzK6lO6CSTIx68wspp6cWnIX8JRSWlhg36oVdi+/jEVQEAqtFo2PDwFLFmNZuTLGtDQiXn+DK42bcLVxE6KGDCHuo/EkfCOm9gmCIDwvEjLzuZ6UQ0q2DlxupLO8Ja1W59KdAZhxegbnks8VOd4kmcjSyUuwXEeOwLpBfaTcXKLHvI0xO+fRX4AgCILwyIlO9XPCWqtmZt+aTO9dnTcaBhbeWboFNB8nf7/7W3iAXNTWGmu+afwNExpPwMvGi/7B/R++0c8otbMz/gsXyh+M8vIwJieDQoE2IACA9JUryT19ushxxuwcMTIhCILwjLHSqgDI0RludqpzkyEvDYAe5XrQtUxX+lboS0WXioWOPZN0hu4butN0WVMup16+sb76B9QeHuiuXyfuk09E/mpBEITngOhUP0eCvezpUt2n+J31RoKNG6SFwelFD3yOTqU7sbnrZrxsvczbMgoyHri+Z5XK1gb/X3/Ff+ECAlcsp/zJE5TethWHbt0AiP/yK3PkcAB9YiJhXbtyvWMnot58C11U1JNquiAIL7h9+/bRqVMnvL29USgUrFu37o7l16xZQ6tWrXBzc8Pe3p4GDRqwbdu2QmUWLlyIQqEo8pX/HES6trnRqc7VGcHCFuy85R3J8ktShULBFw2/4MM6H6JUyB+rcvQ5TDg6gde2vMa19GsYJAM7InYAoHZxwWfaj6DRkLV1K6m//24+lzEjg4z1681LiwRBEIRng+hUP6eSswsYv+bszYjgFrbQ9AP5+72TQJ/3wHVrVBrz939H/E3b1W05GHPwYZr7TFJoNNjUr49V1aoora0BcP/gfVQODhRcukTqn38CYExPJ2rwEPQ3OtLZO3dyvUNHEqdPx5Sb+8TaLwjCiyknJ4dq1aoxc+bMeyq/b98+WrVqxZYtWzh58iQtWrSgU6dOnP7PjBx7e3vi4uIKfVlaWj6KS3isrLVqAHJ1N16Uut4YrU65OQVcqVCiuBEpPM+QR/0l9Vl6aSkSEuWdygNwOO7wzTpr1MBjnDyDLHHyD6QsXEj06NFcbdyE2HEfETlosIjPIQiC8AwRnernkCRJDP3jBEuPRdHhp/0cvX4jFVatAeDgD1lxcOzXEjnP+mvrydZn89bOt1h4biEGk+HuBz7H1E5OuL3/HgDJP82g4HoYUcNHUHD1Kmo3N/zm/opNwwZIOh0ps+dwvVNnEUVcEITHql27dnzzzTd0uzGz5m6mTZvGhx9+SJ06dShbtiwTJkygbNmybNy4sVA5hUKBp6dnoa/ngY3FjenfBTcCdP4nAvh/TTkxBQBfW19+bfUrc1rNYVLTScxoOaNQOad+fbHv2BGMRhK/+56sHX8j6fWgUGDKyiL32PObbUMQBOF5IzrVzyGFQsHkV6tRxt2WxKwC+s47yuw9oZiUWmj+kVzowI+Q/3DTthUKBVObT6VdqXYYJANTTk6h35Z+XEq9VAJX8exy7N4dq+rVMeXmEtatG3lnzqB0cMBv/jxsmzTBb/58fGb8hMbbG31MDBGvvUb+5ctPutmCIAj3xGQykZWVhbOzc6Ht2dnZBAQE4OvrS8eOHYuMZP9XQUEBmZmZhb6eRkVGqt3kkWdiTxVbfnSN0UxtPpU1XdbQwLsBrlautCvVDmfLwvdLoVDg9dWXWNWqhdrLC+dBgyi1fh2Or74KQPbevY/mggRBEIQSJzrVz6ky7rZseKsRXWv4YDRJfL/1EkP/OEF+xVfBtZwcYOXQvU39uxONSsP3Tb7nq4ZfYae140LKBXpv6s1Pp35Cb7r/gGjPA4VSieeXX4BKhZSfj8LaGv9f5mBZrpy8X6HAvlUrAletxCI4GGNKChGvv0He2bMASHo9Wbt2EfvxxyTPmYPpOViTKAjC82PKlCnk5OTQs2dP87YKFSqwcOFCNmzYwNKlS7G0tKRRo0ZcvVr8aC7AxIkTcXBwMH/5+fk9jubfNxcbLT6OVthb3lj6VOZl+c+w/ZCTXKS8g4UDrQJaYaW2umvdSmtrAhf/Sdndu/D48AMsy5fHtkVzALL37BFBzARBEJ4RCukZ+I2dmZmJg4MDGRkZ2NvbP+nmPFMkSWLZ8Sg+33AencHE4Mal+DToKqx4HVQW0PITqD8KVOqHPldSbhITj000B2OZ3GwybQPbPnS9z6qUhQtJX7oMz88/w6Zhw2LLGDMziRo6TB7NtrbGvmNHsnbswJiWZi6jCfDH68uvsKlf73E1XRCEe/A8PJsUCgVr167llVdeuafyS5cuZciQIaxfv56XX375tuVMJhM1a9akadOm/PTTT8WWKSgooOCWdcOZmZn4+fk9G/fzl2YQFwIdpkKdwXctnlGQwYrLKwjPDOfbxndOu2jKzeVKfXmZUNCmjViUKVNCjRYEQRDu170+68VI9XNOoVDQp64/s/vVBGD7hXiyg9pD+fZgLIAdn8Lc5hBz8qHP5WbtxtTmU5nQeAJfNPjihe5QA7gMGEDpbVtv26EGUNnb4//bfKzr1cOUm0v6ihUY09JQubri1LcPand39BGRRA4YQOzHH2NMT398FyAIgnCL5cuXM3jwYFasWHHHDjWAUqmkTp06dxyptrCwwN7evtDXM6Nyd/nP82vvqbhCoWBmyEw2hG4gPif+jmWV1tZY15Nfooop4IIgCM+G++5U328qDoC9e/dSq1YtLC0tCQoKYs6cOQ/SVuEhvBTswdSe1dg8pgm2lhrovQQ6zwRLR4j/B+a9DMv6wa5v4ewKiDkFJuMDnatT6U50L9e9ZC/gOaa0scHvlzk49u6FfceO+P0yh7J7duP52WcEbd6EY5/eAGSsXkNou/akr16DZDI94VYLgvAiWbp0KQMGDGDJkiV06NDhruUlSSIkJAQvL6+7ln1WGIymm9OxK3WV/ww/AJlxdz3WXmtPZdfKAByOPXyX0mDbrBkA2bv3PFBbBUEQhMfrvjvV95uKIywsjPbt29OkSRNOnz7N//73P8aMGcPq1avvu7HCw+lW0/fmmjCFAmq+Bm+dgCo9QTLBpU2wbxKsGQpzW8DiV+EhO29p+Wm8s/sdojJFXuY7UVpa4vXFF/j8MBnbZs1QqOXp+Co7O7w+/5yAJYvRlimNMS2NuI8/JqJff/IvvdgB4QRBeDDZ2dmEhIQQEhICyM/pkJAQIiMjARg/fjyvv/66ufzSpUt5/fXXmTJlCvXr1yc+Pp74+HgyMm4Gu/zyyy/Ztm0b169fJyQkhMGDBxMSEsKIESMe67U9CpIkMXzRCap/tYPwlBtpEB39wK8eIMGFdfdUT32v+kDh1Fq3Y9tc7lTnnj6NMePhgooKgiAIj959d6rvNxXHnDlz8Pf3Z9q0aQQHBzNkyBAGDRrEDz/8cN+NFUqGJEn8eSSCZcciwdYNus+FITuh9TdQ8w0IaCSvtw7dBWeWPtS5vj36LTsjdzJq5yjWX1tPan5qCV3Fi8W6Zk2C1q7F/YP3UVhbk3f6NGHduhPx2uskTplC1t9/Y0hKetLNFAThGXDixAlq1KhBjRo1ABg7diw1atTgs88+AyAuLs7cwQb45ZdfMBgMvPnmm3h5eZm/3n77bXOZ9PR0hg0bRnBwMK1btyYmJoZ9+/ZRt27dx3txj4BCoSAtV092gYEj/6aohJtTwM8VHSTI1xed6dXAqwEAR+OOYpLu/MJa6+uLtkxpMBrJPnDgwRsvCIIgPBYPFajsXgKcNG3alBo1ajB9+nTztrVr19KzZ09yc3PRaDR3Pc/zEAzmabLtfDzDF51Eq1byU+/qtK1czPS8g9Nhx2dg4w6jT4ClwwOdKyk3id6be5OYmwiAAgXV3KrRqXQnepTrgUKheJhLeSHp4+NJmPgdWdu2FdmnCfDHpm5drOvUwaJ8efQxMRRcC0UXeg1Jb8Djf+NRu7o+gVYLwvNHPJtK1tN8P6fuuMJPO6/Spbo303vLLyPISoCpFeSZXm+fBacAAObsDWXqjissH1afGv5O5jr0Rj2NljUiz5DHqk6rKO9c/o7nTPzhB1Lmzce+cyd8Jk0C5OwQ2QcOYF2zJiqHB3suC4IgCPfuqQlUFh8fj4eHR6FtHh4eGAwGkpOLpqKAZyd35bOqVbAHHap4oTOYGLn4FH8cDi9aqN5IcCkDOYmwd9IDn8vN2o3F7RczotoIKjhXQEIiJCmEr498zY+nfnzwi3iBaTw98Z0+jaAtW/D69hsce/TAolw5UCrRR0SSvnIVsR+OI6zLK0SPepOkqVPJWL+BzC1biHrzTZGiSxAE4T7VD5JzTB+5nnJzXbWdhzyzCwoFLFt3OgZ7Sw3ZBYZCdWhUGmp71JbriTty13P+u646Z99+JKMRQ3IyEQMHEj1yFNFvjX7YSxIEQRBK0GOJ/v3f0ch/H0i3G6V8VnJXPquUSgU/9alBv3r+SBJ8tv48k7ddKpwPU62Ftt/J3x+dA0lXHvh8njaevFn9TVZ2WsmOV3cwqvoo1Ao1ZR3LPuSVvNgsgkrh2L07Xl9/RdCG9ZQ7egTfObNxHjQIyypVUNraYhEcjH2nTriOfguVgwP5Z84SO+4jEehMEAThPtT0d0KrUpKQWXBzXTUUmQKerzdyLTGb5OwCgtxsi9RT36s+VmorsvXZdz2nVY0aKO3tMaank7Z4CWGv9iDvhJypI/f4cfL+OffwFyYIgiCUiEfeqfb09CQ+vnD6iMTERNRqNS4uLsUeM378eDIyMsxfUVEiyFVJUykVfPNKZd5rVQ6An3eHMm71WYymWzrWZVtBubZgMsDWj6AEUpp72ngystpINnTdQKfSnR66PuEmlZ0dds2b4/HhB5RauYLyJ44TtHYNPpMn4fbmm/jOnAEaDVnbtpH047Qn3VxBEIRnhqVGRQ1/R4DC66qDO4NSDfFnIfka52IyMJgk3Ows8HawLFLPq+Ve5WDvg7xZ/c27nlOhVmPbuDEACRMmYIiPR1uqlDlNY+offzz8hQmCIAgl4pF3qhs0aMCOHTsKbdu+fTu1a9e+7XrqZzp35TNEoVAw+qWyfN+9CiqlghUnogt/WABoMwFUWgjdKQctK4GONYCf3c3ZBwk5Cby35z3e3/s+PTf2pOHShry5803yDWKackmyrlMHr6+/AiBl7lzSli1DMhYOpmPKzSVzyxZi3nuf+K++puB6WKH9kslE9r59xI4bR/Z+ETxHEIQXR/0geSCg0HPSxgWCWsjfH/uV05HpACRlFTBq8akidVhrrNGo7h5L5l+2LZrf8n0LAlcsx23sWAAy//oLfULifV2DIAiC8Gio7/eA7Oxsrl27Zv7531Qczs7O+Pv7M378eGJiYvjjxhvUESNGMHPmTMaOHcvQoUM5fPgw8+fPZ+nSh4sqLZScXnX8sbFQk5lnoFGZ/wSxcikNDd6EAz/CupFwZDY0eEvO0anWPvS50/LTGLhtIFFZhWcj7Ivex3t732Na82n39QFEuDPHV15BHxlJ8qzZxH/xJQmTJmNZMRirSpUwJKeQtXs3Uu7NqY1pS5di9/JLOL/xBgXXr5P6+x/oQkMByNy6jYAli7GqVOlJXY4gCMJj07isK8fDU6l5S/AxAOqNgGs74NgvFHgHAfJL423n49EZTGjVRccvJEli1dVVOFo40iqg1W3PadeqFfYdO2JZoTzOgwahUCqxqlwJq1q1yDt5krSlS3B/550SvEpBEAThQdx39O89e/bQokWLItvfeOMNFi5cyIABAwgPD2fPnj3mfXv37uXdd9/l/PnzeHt7M27cuPvKXfk0RwR9XuXqDFhpVPK6d30+/P05nPwdDHlyATtvePlzqNpLznn9gCRJ4vfzvxOdHY2/nT/+9v4YTAbG7x9PvjGft2u+zZAqQ0roqgSQ73nid9+TtmIFUl5ekf0aPz/s27ahIPQ62bt2FdmvtLFB4+1FwdVrqL29KLVyJerbLOUQhOeZeDaVrGf6fm5+D47PIwVH2uRPJBk5Mve+D1rg72JdpPi28G28v/d9rNXWLO2wlCDHoPs6Xeb27cSMeRuVoyNl9uxGaVl0qrkgCILw8O712fRQKbUel2f6QfsMSsku4LX5x7CxUFEzwIlAFxtKudpQw9WERcjvcOxXyE6QC1fsAh2ngbVzibbhQMwB1lxdw8QmE7FQWZRo3YJMMhrRXb9O3vnz5J+/gEKrwb5NGyyrVDEHESwIDSXlt9/I2LARjZsbTq+/huOrr4LJRHiPnugiIrCuXRv/Bb+huIf0eILwPBHPppL1TN9PfR6GX5qjTr7EHlM1xlt8SlyWjiVD6tHwvzPAAIPJwLAdwzgef5xA+0CWdliKrbZoYLPbkYxGQlu3QR8Tg+fXX+HUo0dJXo0gCIJwg+hUCw9s7elo3l1+psj2Uq42fNetCvX8beHgT7D3OzmIma0HdJ4JZV4G5aNZpp9RkIFRMuJsWbKdd+HeSCYTKBSFIvYXhIYS3rMXppwcnPr2xfn118gNCSHvdAj62FhsmzfDocsrqGxtnmDLBeHREc+mkvWs3M+U7AIiU3ML5aAGyIs5h3Z+S1SmApY6DmN1giffBEdSIX0/6LJh8A5zLmuAlLwUem7qSWJuIq0CWjG1+dRizydJEhdSL+Bh7YGr1c0OesqChSR+/z0WZctQasOG22ZUEQRBEB6c6FQLD+VsdDonI9IIT84hLCWXf6LTScvVAzCnfy3aVvaE2NOwZjgkX5YPUmrAzgvsvcDBD7yqgld18KoGVo4P1Z4vDn3B9ojtjK4xmp7leqJSqh7uAoUSkbVrF9Gjbh/FVmlri0PXrjj17YNFqVKPsWWC8OiJZ1PJehbu56nINLrNOoS7nQVH//dS0Y7s8fmweWzxB7ebBPWGF9p0JukMA/4agEEysLbzWso4lSm03ySZ+OrwV6y+uppR1UcxstpI8z5jVhbXmjXHlJuL3/x52DZqVOxpJUkSHW5BEIQHdK/PpvsOVCa8GKr6OlLV19H8c2a+nolbLnL0eirNy7vJG71rwPC98PeXcHwemPSQESl/RR2Fc6tuVuhVHbrPB9fCHxjuhc6o40LKBbJ0WUw4OoE1V9dQ26M2CoUCJUpstDb0D+6PndauyLFJuUlcTL1IE58m4kPFI2DXsiVu77xD0rRpKDQaLCtXxqpGDdTOTqSvXoMuLIy0RYtIW7QIy6pVsW/XDvu2bdB4eT3ppguCINy3il72aNVKErMKCEvOKZqLuvYgCNsLF9aTIVlzzbERtTy1cHkzxJ0tUl81t2rU967PgZgD7I7aXahTrTPqaLu6LUl5SQCsv7ae4VWHo1TIM8JUdnY4dOtG2p9/kjx7NjYNGxZ5ziVOn07K3HmoXVzQeHmh8fbCslIlnF97DYX24YONCoIgCDIxUi3cl5wCAzYW8rsYk0li16VEXgp2R2EyQFY8ZMVBZiykXoe4EHk0Oz1SPtjWE97YCG7l7vu8RpORFVdWMOP0DLJ0WYX29Qvux7g644p8mLiecZ0RO0ZQz6se4+qMu6/1asL90cfEoHJzQ3nLhzTJZCLn4CHS/vyT7P37wWQy77OuWxeP/43HskKFJ9FcQSgR4tlUsp6V+9nrl8McDUtlQtcq9K3nD4DRJDFnbyhVfR1oEOjAgcMHGPpXFi9V9GVO7VhY3h88q8CIoqkIV15ZyVeHv6Kqa1UWd1hs3n4k7ghDtw/FTmuHJElk67OZ13oe9bzqmcvo4+IIbdsOqaAA3zmzsWve3Lwv//IVwrp2LfS791+2LVviO+1H0bEWBEG4CzFSLTwS/3aoAX7efY0pO67wai1fvnmlMpaOfuDoV/SgjBhY3AMSz8PCDjBgE7iVv6/zqpQq+lToQ5vANqy9upZsfTYmyYRaqWZApQFFOtSh6aEM3jYYvUmPm5UbNhqxrvdR0vj4FNmmUCqxbdIY2yaNMSQlkbl9O5l//UXeyVPkHjtG2Ks9cB02FJcRI1BqtZh0OrK2biV95SoMqamoXV3NX7YtWmBTv14xZxYEQXi86ge5cDQslQ1nYuhT1w+FQsG1xGwmb7uMjVbF2S/a0KBhM843VMjptNJurINOvAQGXZF0lM19mxNWMYyWfi0BWH48EldbC0JyDgLQ0q8lFioLVlxZwZqrawp1qjVeXji/1p+UefNJmjIV2yZNUKhUSJJEwoQJYDJh+/JLuA4fjj42Dl1YGMmzZ5O9axfRb7+Dz/RphV6GCoIgCA9GjFQLD2z+gTC+3XwBkwTV/Bz5uH0wtQOcUCqLmWadkwJ/dIGEf8DGTR6xdg8u0fbojDqmn5pOM99mfLDvA1LzUynvVJ65refiZOl09wqEx0IfG0vCxO/I2rEDAIuyZbBp0pSMdeswpqbe9jiX4cNxGzMahUqspxeeDuLZVLKelfsZmZJLqx/3UmAwMb13dbpU92H58UjGrf6H+kHOLBvWoPABkgTfB0B+BgzfL8cbuY2QqHRe+VnuTNdsMJ+r6VeZ1HQSfnZ+9NncB61Sy66eu3CwcDAfY8zI4Fqr1pgyM/H6biKOr7xiTrmlsLAgaPNmtL43X3xmHzxI9Kg3kQoKsG3WDJ8ZP4mOtSAIwm3c67Pp0YRqFl4IgxuX4vdBdXGw0nAmKp2evxymyaTdTNp6iasJhadoY+MCb2wAz6qQkwQLO0JaRIm2Z8LRCfxx4Q8Gbx9Man4qwc7BzG8zHydLJ66kXeHnkJ95Bt4hPfc03t74zvgJn2nTUDk7U3D1Gqm//YYxNRW1pydub4/Bf8FveP/wA+7jxmHfsSMAKb/8QuTgIRhSUp7wFQiC8CLzd7FmdEt57fPXmy6QkavndGQ6ANX9inmBq1DIzz6A+KLrqm9V3kOODaJQZ3I1/SoKFDTwakAll0qUcSyDzqRja9jWQseoHBxwHTYUgKSffsKYmUni95MAcBk8qFCHGsC2USP85sxGYWlJ9t69RI8ejaTT3fW6jVlZxE+YQPaBg3ctKwiC8KIR07+Fh9KkrBsb32rMT7uusvVcPDHpeczaE0paro6J3f7zNt7aGV5fD390hvh/YFlfGLQNLEpmrfPAygPZF72PpLwkKrpU5NdWv+Jg4UCmLpO+m/tSYCygrmdd6njWKZHzCQ/Hvm0brOvVJWnqVPSJiTh2745dy5Yo1EV/Ldm2aE7cp5+Re+QIYV27yR1towFJb0DS6zFmZ2HKzMKYmQkKBa4jRmDXssXjvyhBEF4Iw5qWZl1ILGk5OkKTswmJSgegup+jucykrZc4EZHGuLYVqOVZFcL3y8HKahStT5IkjsQdYXfUbrwcq5EkXQGgsmtlHC3lOruV7cak45PYG72XXhV6FTreqX9/Uhf9iSE2joh+/dHHxKD29MRlyJBi22/ToAF+c2YTNWIkOXv3EffFl3h9+81tA3pKJhOxH3xI9p49ZG7aTJldO1FaWt7fTRMEQXiOiU618ND8Xaz5oUc1vnmlMn9fTGDTmTjefflmMLLY9DysNCqcbLRyx7rPcvi1OSScg3UjocfvN/NbSxJEHZPTcjn631c7AuwD+LP9n+yJ2kOn0p3M0cDttfZ0Kd2FFVdWMP+f+aJT/RRROznh9fXXdy3n0KEDlhUqED3mbXShoaT+9tsdy0ePHo33xAk4dO5s3mbKySFxylRyQ07jOnIkdi+/LCLCC4LwQLRqJbP71cTdzhK1SsGVG7Ozavg7msuci83kWFgqoUnZ1PK6w0h1zCkw6Pjm5DdEZkXi7WxHWr7cqW7kczNNVsegjgTYB9DQu2GRKpSWlriNfou4Tz6l4OpVANw/eB+ltfVtr8Gmfn18p08jauQoMtasQevvh+uIEcWWTZ49m+w9ewAwpqaSsW4dTr1737ZugNQlS0ieMRPfmTOwrlXrjmUFQRCedWL6t1BiLDUqOlb1Zs5rtXC3v/kG+9N152g5ZQ+hSdnyBgcf6PWnnNf64gbY/4O8PeIQzG8Fv7WG+W3AUHDfbfC29aZvcN8i6bUGVB6AUqHkYOxBLqRceOBrFJ4ci9KlKbViOW7vjcV50CBchg7FddRIXMeMxuN/4/H6biK+s2bh0KULGI3EfjiO1CVLAMg9fZrrXbuRtmQJBRcuEjN6DNEjR6GLjnnCVyUIwrOqrIcdDtYazkZnYJLA28ESj1uefb5OVgBEp+XdMv37n8LRuPPSYWEHFH90ooFzbQAi8o9jzPPH06ICjX0am4s6WTrR1LcpaqU8HmI0GQu1x+GVV9CWLg2AVa1a2Ldvf9drsG3WDM9PPwEgadp0MjZtLlIma88ekmf+DIBNQ3m9eMpvC5CMxiJlb5X252KMaWkkTPxOLL0SBOG5J0aqhUcqK19PWHIOabl63ltxhlUjGqBWKcG/HnScChtGw+5v4foeiLhlnVZWLPyzCmr0K5F2+Nn50SawDX+F/cW8f+bxXZPv0KpEYJZnjdLGBtehQ+9YxrZ5M5R2dqT9+ScJX31Nzr79ZO/bByYTai8v7Fq2JG3FCrL37CHnyBEcOndG0ukwpKViTElFodGgDQxEGxCANjAQqxrV0Xh4PKYrFAThWZOnN+DlYElp98JLmcyd6tRccK0MakvQZUNaGLjInV8u/wX6XADKpMmb1LYXyY99lfrl+1HNrXKx57yefp3Ru0bzUd2PaOLbBACFWo3399+TMn8e7u+8c88zcZz69EEXEUnqwoXEjR+P0tYGmzp1UNrYoIuIIPbDcSBJOPXtg/v773OtRUv0kZFk7diBfdu2xdapi45Bd/06APnnzpG9Zw92LcSSHEEQnl9ipFp4pOwsNSweWg87SzUhUenM3R92c2fN16HuMPn7iIOgUEHtQdBwtLztyCx5OngJGVx5MAA7InZQ689anEs+V2J1C08PhVKJx8f/w3XUSAB5yqLJhH3nTgStX4fnp58QtG4t1nXrIuXnk75iBRnr1pGzdx/5586Rd/o0GWvXkjRtGjHvvMO1Fi2JHD6crL//RtLr73hufWysGJERhBeMg5X8gnZQ41KFtvs6yVOvo9PyQKUG94ryjrgzNwtdWGf+tmzUZUwGGxSqfFTWYcSk5932nHPOziEyK5JRO0fx8YGPydbJM8GsKlfC98cf0QYE3Nc1uH/4AXatXkbS64keMZLLtWpzuV59wnv2wpSZiVX16nh89BFKa2uc+skvu1Pmzrvt77ucA/sL/Zw8Y6b43SgIwnNNdKqFR87LwYrPOsofJn7ccYXL8bdEBm8zQe5IV+sDbx6Fjj9Ck/dAYy2vuQ7bV2LtKO9cnn7B/bBQWQDgauVq3ncy4STxOfEldi7hyVIoFLiNGYPHJ59gWakSPtN+xGfSJFQ3UiFYlC6N/+8L8Zn2I86DB+H23li8vv0G31mz8PlxKm5vj8GhS2csK1UCk4mcvfuIfms0V1u0JG3ZsiLnkySJ+AkTuNbyJaKGD8eUm/u4L1kQhCekVoATh8e/RIvy7oW235z+feP3wX/XVednQOguc3n/9OMYsysA8HLdSL7rXqXoyfLSYM1wvvR8idcrvo4CBRtCNzDh6ISHugaFUon3pEnYtWuL8sbvSVNGBsaMDFSurvhMn47iRtotp/79UFhakn/+PLlHjxZbX/Y+uVPtPGAASmtr8i9cIHvXrmLLCoIgPA9EnmrhsZAkiSG/n2DnpUSq+DiwZlRDNKo7vNPZ/B4cnwfl2kLf5SXeloyCDOwt7FEqlFzPuE7/zf2xUFswvcV0qrrdPoeo8OLRhYeTvno16WvXYUxOBsB1zGhcR440T69M+vlnkmfMNB9jWbUqfr/MQe0k8qM/z8SzqWQ9b/czMSufut/uRKmAS1+3Q3t6AWweC6VfgtfWwJnlsHYYOJVCyoxFYSygoWYkWb7yuuY9PffgYuVSuNK9k+QlU/4NYdBfHIo9xPAdw1EqlGx6ZRN+9n4l0nZjdjb62FgMCQlYlCtXZAlM/Fdfk7ZkCTaNG+M/b26hfZJOx+X6DZBycym1ZjWZ27aT8ssvWFSoQKk1q1EoxXiOIAjPDpGnWniqKBQKJnargoOVhn9iMpi9J/TOB9STp+5yZSuk3KXsA7TF0dIRpUL+52+hssDT1pPkvGQGbh3I1BNTmf/PfBZfXMyqK6vI0eeU6PmFZ4s2MBD3996j7O5duL71FgDJP80gaepUJEkidfFic4fa+Y3XUTk4kH/2LBF9+j6yQGiSJJF37jzG9PRHUr8gCA/PzdYCK40KdztL0nJ14FVN3hF/Vl7a9O/U7yo9yHCV82x1kXTU86pH1zJdi3aoQQ7uCfK6bKChd0Ma+TTCJJmYf25+ibVdZWuLZbly2DZpUmxMCeeBA0CpJOfAAfIvXiy0L/fUKaTcXFSurlhUqIDLwAEobWwouHSJrL//vqfzmwoKiB3/P5JmzSqJyxEEQXjkRKAy4bFxt7fkqy6VmL0nlJeC3e9c2LWMPEp9ZSscmQ0dbokQfnIhVOoG5YsPkHK/fGx9WNRuER/t+4g90XtYcH5Bof3tS92MoPrr2V/xsvGiU+lOJXJu4dmh0Ghwe+tNlLY2JH73PSlz55F/6TI5Bw4A4Prmm7iNfgvHnj2JHDoUXXg44T17YlG6NJJeL6/HVirReHqg9vJC4+WNxscbi6AgtP7+5qmVxsxM8i9cIP/CRdRurti3aWPeB2BITib+yy/J2vE3CmtrnHr1wnngADTud/k/JQjCY6VQKDj7Reubs7IsK4JCCTlJkHINru2Ut1d6hfMR2TTiCG0trzCu9SZWnYxm4paL9K8fgJ/zjbRYaeFy9HCArDjQ54HGimFVhnEw5iDrQ9czstpIPGwefWBFrZ8f9m3bkrllC8mz5+D703Tzvuz98tRv28aNUSiVqBwdcX7jdZJnzSZ5xkw5neFdRqsTv59Extq1ANi1aIFlcPCjuxggZuxY9HHx+C/4TeTfFgThgYhOtfBYda7mTfsqXoWmfhuMJjki+H/VHyV3qkMWQ7XecOBHuLRJ3nd2OTT/HzT94GaO64dgo7FhWotprL66mitpV8g35FNgLECtVGOtkT/QSJLEsbhjHIs/ho3Ghpb+LR/6vMKzx2XAAJSWVsR/+SU5Nz48OvXti+tbbwLyeu3ApUuJGjKUgqtXyU1NLXR8/j//FK1UpULr64uEhD4istCuxClTcRk0CMcer5K9Zw/xX35lHqGWcnNJXbCAtMWLcXy1O66jRqF2dS1avyAIT0ShZU5aa3AtB0mX5GncxgJwKQvuFTmurEojIDg/BExG/jgcztnoDGoFON3sVF/cVLjytAhwr0BNj5q0CmhFRZeK2GhsHtel4TJ8OJlbt5K1fTu5p05jXVMebc/ZL79otGlyMx2Y8xtvkLroTwquXiV71y7sXn75tvVm7thB2o10iAApc+fiM3XqI7oK0EVHk7nlLwByjx7FtlmzR3YuQRCeX6JTLTxWCoUCjepmmo8VJ6JYfDSS77pVwdZCfaMMZBcYUFnXoKxHZTlg2byXblSgAv/6crTwPRPkfV3ngPbhP0iolCp6lu95xzJetl5ISHy0/yMWtFlAJddKD31e4dnj1LsXSitL4r/8Crt2bfH45ONC6Ws0Hh4ELl9G9oEDYDSi0GhQaDRIBgP6+HgMcXHoY+PQRUWhu34dU04OuoiIm8f7+GARXIG8M2cwxMeTMGECSdOmmQOgWVSogPd3EzEkJpI8ew55p0+TtmQpWXv24P/LL1iULfvY74kgCPfAs4rcqf5npfxzxS6gUPDOaz0xff8h2oJMiA3Bx9GKs9EZhSOAX9xYuK60MHCXA5tNbf7oOp23Y1m+HA7dupKxajWJ339PwLKlGBISKLhyBZRKbBo2NJdVOTjg1Ls3KXPnkrp48W071fqYGOI+lvNm27VqRdaOHWT+tRXX0aOxKFWq2GMeVs7BQ+bvs/cfEJ1qQRAeiOhUC09Mvt7IlO2XScgsoN30/UX2Ny3nxh/1R8H6UfKGcu2g1ZfgVh5O/QGbxsrry1LDoM8ScPR/pO1VKBR83uBzknKTOBh7kDd3vsmSDkvwtvV+pOcVnk4OXbpg36EDCnXxv0aV1tbYt25913okScKQmIQu7DqYTFgEB5sDnJkKCshYu46UefPQR0eDSoXr8GG4jhghTwmvUAGbJk3IPXac+M8+QxcRQXjffvjOmIFN/XoAGLOyyFizhrzz57Fp0BD71q1Q2jy+0aynjWQyiUBJwmOz82ICv+67TlVfBz7uUBHJsyqKf1YCN2LEVnpF/lOpQlmqqTwb6/pufBzlZUcxaTc61VkJEHUj0rZvHYg+Lk8Hf8Lcxowhc/MW8s6cIWvrVkw5cgwSqypVigRqdOrdi5T588k9fISC0FAsSpcutF8yGIh5/wNMmZlYVq2Kz5QfiB7zNtl79pAyfz7e33zzSK4h59Ctnep9wMeP5DyCIDzfxCcL4Ymx1KhYObwhdQKdsNKosNQosVAr0aqVONtosdGqoHpfpC6zONLsT+b5TZA71CDnuB6wCWzcIOEfmPdy4dyfj4haqeaHZj9Q1qksKfkpjPp7FOEZ4SL/5gvqdh3q+6pDoUDj4Y5N/frYNGxY6IOo0sICp969KL31L3xn/UyptWtwGzOm0BprhUKBTb26BCxbilXNmpiysogcOpTUP/4g/ptvudasOQkTvyNzw0bixo/nSpOmxI7/H7nHjxf779aUlycHX/t1LrqoqIe+vodVkv+3sg8cJOzVV9HHxZVYnYJwJzk6I0fDUjkTlSH/7HxzdlO45MXmhFuCkQU1l/+8vgefG+m4zCPVlzcDEvjUgoAbI8CpYYXOZZJM7IzcyZBtQ8jSZfE4aNzdcRk8GJCXqmT9La8Tt2nSpGhZHx9sW7YAIG3xkiL7k2bOJO/0aZS2tvhMnYJCq8Vl+DAAMtZvQB9f8mkvJaORnCNHzD/rIyILzRoSBEG4V2KkWnii/F2sWTmi4R3LnHBqR+/lh9GoLtG8vDtl3G1vHFwfhu6GJT0h8QIsaA89f4cyt1+rVRJstbbMemkW/Tb3IzQjlM7rOnOwz0HstHYAhCSGAOBp44mblRsqpeqRtkd4/inUauxa3nkNv9rJCf8FvxH70Udk/bWVhAkTzfu0ZUpj26QpWbt2oo+IJGPtWjLWrsWyYkWcBw3Cvm0bANLXriV5xkwMiYkAJE2dinXt2jh0fQWNjy/66Ch00dHoY2NROTpiWb48FuXKY1G2TJHgPoa0NLJ37yF79y7yzpxFoVajsLZCaWmF2tUVx549sG3evMiosT42ltwTJ8g7d478f86Rf+kSWl8fPMaPLzSdFCD74EFSF/6OKTcXpaUlCitLVHb2OPXtg1WVwjl+8y9eJGbMGEy5uaQuXIjH+PH395cgCA/gv7mqbQNqmPdtMtblh6Wn+X7bZQJcrPmgVg2qAkQdxe9GMXOn+t+p38GdwNJR/r6YkeqZp2dyLf0ayy8vZ0iVISV/QcVwGTSQ9OXL0UdHyzNqANumRTvVAM79+pH9904y1q3Dbey7qGzl53nOkSOk/PIrAF7ffI3W1xcA6xo1sK5Th9zjx0ldsMD8/1afkEDm5i3YNmtaZMT7fuSfP48pIwOlnR0WZcuSd+oU2QcO4BwQ8MB1CoLwYhKdauGpVzvAiZYV3Nl1KZHP1p9j8ZB6N9evOvrBoK2wvD+E7YPFPeVI4f4NQJ8rR0e1dJDXsZUgTxtPZreazYSjE8gz5Jk71ACTj0/mbPJZAKzUVrQJbEOPcj2o4lql0LpbQShpSgsLfKZMIcnXj5SFC7Ft2BCn11/DpmFDFAoF7h9+QN7p06SvWUPmps3kX7hA7Pvvkzh1Ckora3Shcvo6jY8P2gB/cg4fIffECXJPnLjziRUKVPb2KB0cUN3I4Zh/4QKYTLc9JHvPHizKlsVl2DCs69Qma/sOMrdsIS8kpEjZgqvXiBw0GPtOnfAY9yHGtDQSJk8mZ1/RZSMAmVu24D1pEvZt5On3+thYooYNx5Sbi3W9eri/99493E1BeHj/dqpjM/LRG01orJ3BozJS4kWsavaEYxCZmktkai6jmtUDe1/IjKZMwTlAKU//zkuXn28AFTpBxo0ZJGmFR6qVCiWDKg/ifwf+x6ILi+gf3B9L9YNHsjaajPxx4Q/KOpWlsU/j25ZTWlvj9s7b5rXQKkdHLCsVH2/Eun59tKVLowsNJWPdepz798OQlkbsBx+CJOHY41Xs2xbO7OEyfDi5x4+TtmIl9p06k75qJRmr1yDp9STPmYP//HlFXqLdq5yDBwGwqV8fyypVyDt1ipz9B3Du1++B6hME4cWlkJ6Beav3mnRbeH5FpuTS6se9FBhMTO9dnS7VfQoXMOhgw1tyVPDiVH4VOkwBK8cSb5tJMplzXgO8u/tdLqRcIDE3EYNkMG8v71Se3hV607VMVzF6LTxyd1s7bEhLI23JEtIWL8F4I0K5ysEB11EjcezTB6VWiz4+noz1G8jcsgUpPx+Nnx8aP1803t4Yk1PIv3KZgkuXMaalFXsOi+Bg7Fq2xKZRQxQqFaa8fEx5ueSdPEna0mXm9ZeFKBRYVa2KZbWqWFWujEX58qSvWk3an3+CJKG0scGUlyd32NVqnPr0wbp2baT8PEx5+WTt2il3thUKPD4ah0PXrkT060fB1WtYlC1DwOLF5o7/wxLPppL1PN5PSZIoNX4LAG80COCLzpVQZMZCbgp4VWX3pUTGrghBrVKy74MWWG0ZAyF/UlD3Lcrvk2dmXOmZiXbDCHALhjePyCPU06uBygI+ji+UAcNgMtBxbUdismP4uN7H9K7Q+4HbPvvMbGaFzEKlUHH6tdN3fCksGY2EdetOweXL2HfsiM8Pk29bNnXJEhK++hptqVIEbd5E9Kg3yd6zB21QEKVWrURpbV3kHoZ3f1V+UXcLlaMjxvR0lLa2cse6WrX7vsaI/q+Re+IEnl98jlX16oS90hWFlRXljh5BecsyG0EQXlz3+mwSnWrhmfHTzqtM3XEFNzsLdr7XDHtLTeECkgR7v4ejv8ghxDXWoLaE1OsgGeURgK6zoVTTx9Jeo8nIP8n/sPLKSraGbUVn0lHfqz6/tPqlUCdcEJ4kU34+mZu3YMrJweGVLvfd4ZQkCWNaGsbUVIyZmRgzMpDy8rCqVg2Nj89tjzNmZpK2ZAmpv/+BMS0Nq+rVsW/fHrs2bdB4FM25nffPOeI//9z8wdquVSvc3xuLNjCwcHuMRhK+/Za0JUsBUHt4YEhIQO3uTuDyZWi8vO7r+u5EPJtK1vN6PwM/2mz+/tSnrXC2KdxZy9MZMUqSnAHj7EpYMwTJ3oeIqm9jWbkDHnvGobi0CZp+CC0/BqMBvvUAkwHevQAOhf+fLb20lAlHJ+Bj68OmrptQK+9/UmJ0VjQd13bEKBkB2NZ9212DcuZfukTyzz/jNmbMHTMQGLNzuNasGaacHOzatiVr61YUGg2BK1dgWaFCscdk7thBzOgxANg0bIDryJFYBFckasRw8k6cRGljg9+8uVjXqFHs8bdrx5UGDUCvp/T2bWj8/LjatCnGpGT8F/yGTYMG91yXIAjPL9GpFp47+Xoj7abvJyw5hxbl3fixV3Ucre/hTXL0CVgzVO5co4BGY+Clz+ExjhZnFGSw7to62gS2wdPGEwC9UY9aqRZTwoUXmqTTYcrLQ+XgcPeyBgOZ27ah9fHBqnr125eTJFJ/W0DiZHm0TGljQ8DiP2/7gf1BiWdTyXpe7+fkbZdYdiyKOa/Vok6g850L5yTDj5XAkC//rFACCvnF8PB94HVjNHZ6dXn694DNEFh4ana+IZ82q9uQmp/KxCYT6RjU8b7bfD39Oh/t/4iLqRcBmNZiGi/5v3Tf9dxO/DffyrNPbvD4+GOcX+t/x2Oy9uxB7eJSaKq3KSeHqBEjyT1+XJ6G/u672HfsUCTyeLH17d5N9MhRaPz8KLNjOwCx4/9Hxtq1OA8ciMe4Dx/w6gRBeJ7c67NJDJcJzwxLjYpvu1ZGo1Jw4FoyUal5RcpIkkSBwVh4o29tGL4far4BSHBwOmx6547rPUuag4UDb1R6w9yhBphwbAJjdo95bFFaBeFppNBq76lDDXLANocOHe7YoQY5IrrL4EH4TJ+Odf36+M6aVeIdakG4Vx+0qcDxj1++e4cawMYVRh6C5v+TY4FIJrlD7VQKPKveLOcUKP9ZTLAyS7Ul/YPlDur8f+Zjku7/WRfkGMSSDkto5ivnbL6YcvG+67gTp759zN/bNm+OU/+7r2G2a968yNpppY0Nfr/MwbpePUy5uSR8+y1XmzQlauQoMrdsQRcZiWQ0FltfzqHDAIUCINo2kV9Q5BwoPl6DIAjC7YhAZcIzpWFpV9aOasSl+Cyq+MofxC/GZbL7ciIhkemcjkonLUfHxx2CGdio1M0DLWyh80/yG/21w+U812pLaDdJnir+mMVmx7Lh2gZ0Jh2vbXmNGS/NwM/O77G3QxCeZ/ZtWpuDlQnCk6RU3sdzxqU0NB/HUf8hnDwTQiPlOao1aFP4WeVcCq7vLpJW61+9KvTicNxh+lToU+z+25EkyTx7Sq1U08C7AXuj93Ip9dJ91XM3FkFBOL/xBvnnz+M1ccJDzdhSWlvj9+svpC9fTsa69eRfuED27t1k794NgEKjQRsYgEVwMG5j3kbrK0+XNwcpa3SzU23TsCEolRRcvYY+Ls68XESfkIDK1haljc0Dt1MQhOeb6FQLz5zKPg5U9rk5srXrUiKTt10uVObLjRdIydbxXutyhR/WVXuCyQjrRsKxX+WOdauvIDsRLqyT05Y4+sudbQvbR3YN3rbe/NH+D8bsHENoRij9NvfjxxY/Usuj1iM7pyAIgvDsOB2VzqSj+bxSvSnT3P8z08LpxkvjYkaqAey19vzW5rf7PucnBz/B08aTYVWHYaGyoKlvU5wtnansUvm+67obj/EflVhdSgsLnF9/HefXX6fg2jUyNmwke+9edGFhSDodBVevUXD1Gjl79+H9w2QsypZFd/06KJXY1K9vrkfl6IhV1arkhYSQfeAA1tWrkzRjJlnbt2NRrhyBK1egtLAosXYLgvD8EJ1q4ZlXP8iFDlW8qO7nSHV/R46EpjBlxxVm7r5Gjs7A553+k9qjeh95vdqmd+DQTxC6S85zfesUudjT0HuJPBrwiFRyqcSSDksYs3sMF1IuMGT7EIZXHU5D74ZUdat69woEQRCE55aPo5yOy5yr+lbm6d/Fj1TfyiSZ+PHkj7Qt1ZZKLsWnugI4Hn+cDaEbUCqUvOT/EhVdKuJn5/fMzaKyKFMG97Hv4j72XSSjEX1cHLrQUJJmzCT/3Dmihg3HunZtACyrVC4SnNGmSWPyQkJI+nGanNngRuihgitXSJ4zB/e3337s1yQIwtNPrKkWnnm1Apz4uV9NhjYNok6gM6NfKsvEblXQqpU0K+dW/EG1B8qj0QAJ5+QOtW8daPEJ2HrIney5LW7mBn1EPGw8WNh2Ia0CWmEwGfg55GdWXF5RqExyXvIjbYMgCILw9PG5keM6Jq2YTrXznUeqb3Uw5iALzy+k96bevLblNXZG7CxSxiSZmHxcDuzXo1wPKrpUfOB2P00UKhVaX19smzUjYMliHHv3Akki9/hxAGwbNSpyjG2TJgByqkFJkjMNfCgHLUuZO4/8y5eLHHMn+sRE0tesRdLrH/Jq7k6SJPL+OYdkMNy9sCAIJUp0qoXnUp+6/uz7oAXNy99MzSNHYI0kPiOfuIw8wkv3J7b1HHQvfwNvn4Uhf0OzD2DYHvCuCXlp8McrcGS2+U31o2CltuKHZj8wvu54Wvq1pKZHTfO+CykXaL2qNd8f+55MXeYja4MgCILwdPG9MVIdn5mP3vifYGP/jlTnpkD+nZ8NXjZedAjqgFqpJiQphHf2vMPSS0sLldkQuoGLqRex1dgyqvqoQvsup15mwbkF7Ina8xBX8+QptVq8vvgCr+8morC0BMC2WbMi5SwrV8axT2/s27en1JrV+M74CZdBA7Fr9TIYDMR98ultg5/9l6mggKjBQ4j73/9ImjGzRK+nOKnz5xPeowdJP//8yM8lCEJhIqWW8EJIyiqg/sSdGE1F/7m72mpZMbwBQW63rKHW58HGt+Hscvnn4E7QeSZYOT6eBt8wK2QWs8/MBsDJwonanrVRKpQoUaJRaRhYaSBlnMo81jYJgiATz6aSJe5nYSaTRIVPt6Izmtj/YQv8nK3N+9JzddjPDEaZm4w0fB9/JbvTqLQrDtaa29aXlJvE3H/msvTSUlQKFbNfnk0D7wbk6nPpuLYjSXlJjK01loGVBxY6bv4/85l2ahptA9syudnkR3a9j5MuMhJdVFSxI9W3o09I5HqHDpiys/EY/xHOb7wByB3ngqvXsCxXFoW2cJrPhO++J3XhQkDOdBC0ZYs5UFpJM+Xnc63lSxhTU1F7eFBm9y4USjF2JggPS6TUEoRbaFVKPmhTnjLucsdZo1Jgo1VhpVGRnK3jj8MRhQ/QWEHXX6Dtd6DUyAHMfmkKMacea7tHVR/Fr61+pbRDadIK0tgRsYNt4dv4K/wv9kbvxcPG47G2RxAEQXg8lEoF3o7yiOqt66pPhKfSfvp+Ikzy8qbFf+1l1OJT/LTrKlzdAXOaQNTxIvW5Wbsxvu54OpfujFEy8t7e9wjPCGfB+QUk5SXhY+tDv+Ciqa2CnYMBSjwC+JOk9fe/rw41gMbDHfcPPgAgcdp0Uub/RtTwEVyp34DwV18l/LXXMCTfXK6Vc/iwuUOt8fdH0ulInPLDQ7VbFxFBxMCBpMwvGoQuY/0Geco6YEhIIO/U4/28IggvOhGoTHghOFhrGNGsNCOalcZkkszpTRKz8ll9MobhTYOKHqRQQP2R4FcXVg6A9AiY3xoqdgb3ivKXRyVwCnikbW/g3YCVnVeyO3I3qfmpmCQTJslEgH0Adlo7QF5H9e3RbzGYDOhNevQmPdZqawZUGkCgQ+AjbZ8gCM+Wffv2MXnyZE6ePElcXBxr167llVdeueMxe/fuZezYsZw/fx5vb28+/PBDRowYUajM6tWr+fTTTwkNDaV06dJ8++23dO3a9RFeyfPPx8mK8JRcom+sq152LJKP153DaJK4ZuNGKS7S0DkLgEWHrzPu2mdo00Nhw1sw4iCoCn/MUxRk8lnpnkRkRhCVFUVSXpJ5KvjYWmPRqgqPtAJUcJEjj0dkRpCjz8FG8+KmlXLs8SqZGzeSe+IEiZMLj9rnnzlLeK/e+P0yB7WbG7EfjZeP6dULp759COvajay/tpLbvz/Wte4/04c+Lo6IgQMxxMaRe/gIVtWrmeuRTCZSFywAQGlvjykzk8wtf5kDsgmC8OiJTrXwwrk1X6i7nSUjm5e+8wE+tWD4flj/JlzaBOdWA6tv7q/SU86BrbEq/vi0cAg/AGH7waiDjj/e9zRyjVJD68Db59vdHbWb5ZeXF9m+NXwrXzX86o7HCoLwYsnJyaFatWoMHDiQ7t2737V8WFgY7du3Z+jQofz5558cPHiQUaNG4ebmZj7+8OHD9OrVi6+//pquXbuydu1aevbsyYEDB6hXr96jvqTn1oSuctBNdztLftp5lak7rgDQqZo3zVzqwKF9BKmSaF7eDa7ukDvUAEmX4MRvUG/YzcryM+DX5likhTNtwEb0LqXxsvViRccVrL+2nlYBrYptg7OlM+7W7iTmJnL50jpqlmkPVk6P+tKfSgqlEq9vvyH67XdQ2dtj27QJNo0bo9BaEDVyBPqISMJ798GyQgUMCQloAwLwGPchSmtrHHv0IH35chImTCRw5Qrz1GxJp0OfmIhUUICk0yHpdKg9PNB4eprPa0hOJnLgIAyxcaBSgdFI3GefU2rtGpRaLdl79qALD0dpZ4fXF58TM/Y9Mrdvx+Pj/6FQqZ7U7RKEF4pYUy0IN+TrjXy67hztqnjSskIx06olCcL2yum2Ei/KEcITzsuRw71ryCm47L3lsrmpcoCzM8sgI7JwPVV7Q7dfSrTtUVlRbAvfhtFkRKvSolFq2BGxg1OJ8vSvua3nUt+r/l1qEQThfjwPzyaFQnHXkepx48axYcMGLl68aN42YsQIzpw5w+HDhwHo1asXmZmZ/PXXX+Yybdu2xcnJiaVLlxapszjPw/18FIwmiS82nGfREXmZ0lstyvBe63IoQpbA+lEQ1Jxrbf8kdmZ7mirPkmfnj1VWpNzxHX0KrJ3l59fKN+DCernSlz6HJmPvrQEF2by1sQ97c8L5KCWVfu71of/qux/3gjGkpRH91mjyTp6UN6hUBC5bilWVKvL+lBRC27TFlJ2N14QJWJQtS8batWRs3owpI6NIfdZ16+LQpQs29esRNXIUBVeuoPH2xnf2LCIHDcaYkoLrmNG4jRpFeP/+5J04icvQIbiNHs2VJk0xZWTgv3AhNvXFSy1BeBj3+mwSI9WCcMNvB8NYeTKa9SGxvN+mHIMbB6G6ZVQbhQKCmstf/wrbBytelzvav7aALj9D+D44Ph902XIZpVoe7faqDsfnwtll8hTyCh1KrO1+dn4MqTKk0LbeFXoz4/QMwjLCqOtZF5Cnicdkx+Br51ti5xYE4fl2+PBhWrcuPNulTZs2zJ8/H71ej0aj4fDhw7z77rtFykybNu229RYUFFBQUGD+OTNTZDgozvS/r7DoSAQKBXzRqRJvNAyUd5hzVYdTRhFLGeVZjJKCtxQfM899OorEC7DnO2g/CY7Pu9mhBog4ePdOtckE+3+AwzOpYAl7nRy4pNVC6C7ISSZbY4mt1vbOdbxA1E5O+C/4jfhPPyNjwwbcx441d6gB1C4uuI4cSeLkycR98ol8f29QaLUorKxQaDUo1BoM8fHkHjtG7rFj5jIqN1f8F/wmj37/bzyx771Pyuw5aH18yDtxEjQanPr3R6HVYtfqZTJWrSbzr79Ep1oQHhMRqEwQbhjSOIjWFT3QGU1M2HKJvnOPEJ2We+eDSjWFobvBLRiy42Fxdzg4Xe5Qe1aBHgthXAQM3i5/sGk4Wj5u49uQk/JIr0etVPNurXf5sfmPKBXyf/XIrEjarWlH29Vt+ebIN5xJOsMzMFlFEIQnKD4+Hg+PwrN3PDw8MBgMJN8IzHS7MvHx8betd+LEiTg4OJi//Pz8Sr7xz7iMPD0FBhPlPeyY0afGzQ413MxVnR4Fh2YAsEdRm52Jdmz3fVved3yePGNq2//kn6v3l/+MPALGu+QyPrMEdn8L+RkEW7gAcNnGHiQTuZc20GdzHz47+Bm5+rs8J18gSq0W7++/o9yxo7gMHlRkv9Nr/dEE+IPJhMLCAvsOHfCbN4/yp09R/ugRyu3fT9nduyiz82/c3n0XbSn571jl6EjAb3KHGsC+fXtsmjRB0uvNa7cdOnRAc+P/oH27dgBkbd8uclYLwmMiOtWCcINWreSX12rxXbcqWGtVHA1Lpe20/Xyx4TwHryXf/kDnUnKnuZz8EMOnFvRZLq/DrtQVLG55k9/8f+BWAXKSYMt7j/aCblApb66nupJ2BbVCTUx2DMsvL6f/lv50Wd+F+f/MJyEn4bG0RxCEZ49CoSj0878v427dXlyZ/2671fjx48nIyDB/RUVFlWCLnw/2lmrKe9oxpWc1Olb1LrzT1hPUliAZIWQxAPraIwh0sca/Tnso30Het3a4HM+jQkc5/oelg/ziN/7M7U9ckAU7v5K/bzaOugP3sKzDMhYF9ABg8oXfCc8M52DsQXRG3aO49Geays6u2O1KrZaABQvwmfYjZffvw2fKD9g2blRk3bPG2xvX4cMI2rKZUuvXE7RxAxZly5r3KxQKPD//TM63feP/ovPAm6nQbOrVQ+XkhDEtjZwjR++53Xd6yV4QGkrEwIHEjv8fuuiYe65TEF4UolMtCLdQKBT0ruvPX283oXaAE9kFBhYeCickKr1QOb3RVPhAS3vosxTePQ9DdkL5tvJ08f/SWELXOaBQwfm1cG7No7uYYrQKaMXBPgeZ2XImnYI6YamyJCwjjGmnptFqVSuOxt37w1cQhBeDp6dnkRHnxMRE1Go1Li4udyzz39HrW1lYWGBvb1/oSyhMoVDQraYvlX0ciu5UKsHxRvYJyQSeVWjTvhubxjQh2MseWn8tp4QEcPSHLjNBqYKAG6mkwg/c/sT7p0B2AjiXhibvY2fpQCXXSlgEd2KntRWrTKkoUDCh8QQcLR3ZFr6NSccn3fFapp+aTtf1XYnLjnuAO/H80Hh7Y9+2Lap7+PeuUCiwLF8OtZtbkX1aX1/cxowBwLZZMyzLl7t5nFqN3Y0lG5l/bTFvl0wmDMnJmPLzzdtM+flk7dpN7Mcfc7VRY642a07qkiVIer25TMbGjYS92oPcw0fIWLuW6+3akTBxIoa0tPu/AffIlJ+PKS/v7gUF4SnxQJ3qWbNmUapUKSwtLalVqxb79++/Y/nFixdTrVo1rK2t8fLyYuDAgaSkPNqpr4LwMAJcbFg+vAE/9qrGsKZB1Al0Nu9bdDicV2cfIj33P2/nFQpw8C2+M30r7xrQ5MYo9cZ35IBmhoLCZTLj4PDPcO3vh7+Y/7DWWNPMrxkTmkxgd8/dfNnwS2q618RKbUVVt6rmchdTLhKfc/upm4IgvBgaNGjAjh07Cm3bvn07tWvXRqPR3LFMw4YNH1s7X0j/TgEHqDcShVKJrcWNcDkupUmo9xGSSxno+cfNiN03OtVS+EEm/nWRD1aewWi6ZYQyNUx+/gC0+RbUN9NsJdh78LmbKwADvJpRz6seYRlhfLD3AxZdWMSW6zc7cLdaeWUl8/6Zx7X0ayw8v7BELl0A54ED8P/9d7x/mFxkn3kK+I6/yT54kLgvv+Rqs2ZcbdyEy9VrcKlada42bcaVBg2JHjWKjNVrMKamYkhIIOGrr7neuQuZ27cT9/kXxH7wIVJeHtb16mHTsAGSXk/q738Q2qo16atWlfh1GdPTud6+A6Ht2mNISirx+gXhUbjvTvXy5ct55513+Pjjjzl9+jRNmjShXbt2REZGFlv+wIEDvP766wwePJjz58+zcuVKjh8/zpAhQ4otLwhPC5VSQdcavvyvfTB1S8md6ow8PdP+vsqZ6Ax6/3qE5OwCkrIK+GbTBTadjb33ypt+AL51oSADtn4EM2rB6T/lTvSyfvBjJXkN3J+vwpE5j+gKwVZrS7ey3fi93e/s6LEDK7WcFizPkMd7e9/jlfWvsOLyCkyS6S41CYLwrMjOziYkJISQkBBATpkVEhJifo6PHz+e119/3Vx+xIgRREREMHbsWC5evMhvv/3G/Pnzef/9981l3n77bbZv387333/PpUuX+P777/n777955513HuelvXj+DVZm4waVC6dHW3w0gsb7KjK1/BL5Ze6/AuVOtT7sIHP3XmPlyWhOR94y4rjjU3m6eFALKNfWvPl04mleXtWKDKWC4AIdo3ONAJRyKMWwqnLqri8Pf8nl1MuF2hGSGMKEoxPMP6+9tpZM3Z2D0hlMBtZdW8eRuCP3dBteVAqFApt6dYudbm5dpzYqN1dMmZlEDR5C+tJlGJNuLmWTCgowJCYi5eWh9vbCqX9//Bf8hsenn6ByckIXFkbMmLdJX74cFApcR43C/7f5+P/2G37z5mERHIwpO5u4Tz4l+de5Rc6ffeAgsR+NJ//ylfu+rsQpU9HHxmKIjyf2fx8jmYp+BjHl5oqYMMJT5b471VOnTmXw4MEMGTKE4OBgpk2bhp+fH7Nnzy62/JEjRwgMDGTMmDGUKlWKxo0bM3z4cE6cOPHQjReEx83BSsPSYfVxs7PgUnwWnWccoMmkXcw7EMbU7VfMb/uvJWYTkZJz+4rUWhi4Rc5ZbecFGVFyHuw/u8u5sCUjuJQBJNg6Dv7+wrxu6lGx196cipZZkImLpQs5+hy+PvI1g7cNJiIz4q51xGXH8enBT2m2vBkzT8+kwFhw12MEQXi8Tpw4QY0aNahRQ+5ojR07lho1avDZZ58BEBcXV+hFealSpdiyZQt79uyhevXqfP311/z000+Fclw3bNiQZcuWsWDBAqpWrcrChQtZvny5yFH9qJVrI2eYaP6RvLzoFjZaNXqjxIxd1/jur0vk6+VOMJ5VwcIerSGbYIX8e/1Q6I3Zg2H74OJGeYlS24mFZl7FZt98cfx9UjKaq9vBJNc5otoIarrXJNeQS+/NvZkdMtu81trH1odKLpVoFdCKMo5lyDPksfbq2jte1uW0y3x+6HNG7BjB6cTTD3WLXlQKlQrHV18F5EBnDq92x+/XXyh/9gzlThyn9N9/E7h6FUEbN1Bm5048P/kYmwYNcO7Xj9Lbt+EybBgKCwtUTk74zZ2L25jR5rXfto0bUWr1KlyGDwcgaepUEqdMQZIkjNnZxH36GVFDhpCxbh2Rgwej+0+8BEmSSP1jEdHvvov+P8tGck+eJH3lSvkatFpy9u8nbdGiQmUy1q/nSoOGhPfshT72PgY0BOERuq881TqdDmtra1auXEnXrl3N299++21CQkLYu3dvkWMOHTpEixYtWLt2Le3atSMxMZGePXsSHBzMnDnFj8AVl2bDz89P5K4UnhphyTn0m3uE2Ax5XVJ1P0febVWOpmVdScwqoNusQxQYjCwYUJcqvsWshbuVPg+O/QoHfpTXxVXrA7UGyAHN9k+BXV/L5ar3g07TQaV5tBd3g9FkZNnlZUw/NZ08Qx4apYY3Kr3B0CpDsdZYFyqbmp/K3LNzWX55OXqTvA7LRmPDhlc24G7t/ljaKwiPm8irXLLE/XxAJqO8VroY326+wNz9YQAEuFgzoWsVGpVxhcU94eo2TpR/j1fP1KJBkAtLB9eGX5tBwjmoMxQ6/FCoLp1Rx7RT02ji1ZAGi/rKM60GbQd/+cVJSl4Knx/6nL3R8mfBIIcgvm70NVXdqqIz6jCYDGwN38rnhz4nyCGIdV3W3TGQXcOlDcnSZeFu7c6qTqtwsnQqibv1QpGMRnSRkWj9/FCo7z+LrjEzE4VKhdLG5rZlUubPJ3Gy/G/Fvn07ckNCMMTK6+bVbm4YkpLQBPgTuGQJahcXJL2euC++IGO1HFNG4+9PwO8L0Xh5Iel0XO/WDd21UBx7vIplpUrEf/ElCo2GwBXLsahQgeRZs0ieMdN8fpWzMz7TfsSmbt37vj5BuBf3+my6r051bGwsPj4+HDx4sNA6qQkTJvD7779z+fLlYo9btWoVAwcOJD8/H4PBQOfOnVm1apV5LdZ/ffHFF3z55ZdFtosHrfA0iUnPY+HBMBqWcaV5OTfzh4PEzHwGLDjOhbhMrLUqfuxVnTaVPO9eoSTJX8r/TCA5tUhOwSUZwc4bSjWBwMbyujinwNt+mCop0VnRfHPkGw7GHgTAw9qDZR2X4WrlSmh6KD+c+IGTCSfJM8gBRep41uFl/5ex1drSuXTnWy7vzpGABeFZIzqBJUvcz0dj+/l4Plt/nvhM+SVw3UBnllc+huLvz8gObE3lSwPQqpWcb3sNzd+fgKUjjDkN1s63r3TVYDi3Chq9Da2+Mm+WJIlt4duYeGwiqfmp/NLqFxp63/y8WGAsYOnFpbxS5hUcLR3N27N12Xyw7wM+rPMhpRzkdeI5+hx6b+pNeGY4jXwaMeulWeb0kMLTJW35CuK/+MI8o07j54fXt9+gDQwkok9f9DExWFaqhO+sn4kb/z9yDh0CpRKVizPGpGQ0vr4E/L6QjI0bSZo2HZWLC6U3b0Lp4ED0W6PJ3rkTbVAQVtWqkbFWnuXg9Npr5J48QcGFi6BW4/HRRzj16ys+Zwgl7pF2qg8dOkSDBg3M27/99lsWLVrEpUuXihxz4cIFXn75Zd59913atGlDXFwcH3zwAXXq1GH+/PnFnkeMVAvPuqx8PaMWn2L/VXn90pstSjO2VXlUygf8ZX95K6wZCgX/XYemAGsXsHWXg6Q1Hw8+NR+u8cWQJIndUbuZdHwSQQ5BzHp5FiBPB2yzug0Awc7BvFPzHRp4NyjyUDsef5xZIbOY1mIaDhZ3GbkXhGeE6ASWLHE/H52sfD0/bLvMH0cikCRY1EZFk729kCwdqW+YizI7ngO241AZcqHTT1DrjTtXeG41rBoELmVhdNHlfBkFGeyM3Em3st3u3jZdFiN2jOBs8lnqe9Vnbuub63Mvp16m35Z+FBgLGFNjDEOrDjXvW3VlFQ28G+Bj63PvN0J4ZDK3bCFh8g/YtWyJ+9h3zaPbBWFhRPTthzEtDYVGg6TXo7C2xmfqFCzLlSPijQHoo6JQe3thTE5B0unwnjwZh04dATCkpRHW5RUMiYnyiZRKPD/7DKfevTDl5RH36WdkbtoEgEP3bnh+/jlKrbbYNkp6PQXXwyi4dBF9XBxKa2uUNrYobW3R+PpgWbGi6JQLRTySTvWDTP9+7bXXyM/PZ+WN9REgBy9r0qQJsbGxeHl5ldjFCMLTRG80MXHLJX47KE+9a1bOjem9q+NoXfwv+7vS5UL0cQjfL6dCiT4BJn3hMhYO8MYG8K7+cI2/jXxDPtn6bFyt5OivkiSx8spKKrlUItgluNhRBL1RT6d1nYjJjqGsU1l+bfWr+XhBeJaJZ1PJEvfz0TsXk0FESi7tKrqinBQIumwmBc6leugcWqtOgl99GPhX0RlT/5WfCZOC5GfQWyfAteydy99Gcl4yo3eO5lzKORwsHJjbai7BLsGFyqy5uobPD30OwIqOKwh2CUaSJEb+PZIsXRaLOyx+oHMLj0/e2bNEDBiIlJuLys0VvzlzsKpUCQB9fDwRb7yBPkKO42DTqBF+8+YW6tzmHDlC5KDBKCwt8f1xKrbNmpn3SZJE6oKFJP7wA5hMWFWrhs+Mn9C4y0vPTHl5pK9cScb6DRRcuVIoVdh/WVatisugQdi1erlI7nDhxfVIOtUA9erVo1atWsyaNcu8rWLFinTp0oWJEycWKd+9e3fUajXLly83bzt8+DANGzYkJiYGb2/vErsYQXgarQ+JYdzqs+TrTfSt58+ErlVKpmKjAXJTICcJchJh72SIPCSnTRmwGTwqlcx5SsC1tGsM3TGU5LxkAu0Dmdt6LvZaezIKMvCyvfuLNUF4GolnU8kS9/Mx+7M7XPubTJ8m2MfsR1KqUYw4AO7Bdz8WYFFXCN0FLT6GZh/e16nPJp3l04Ofcj3jOgCOFo7Maz2P8s7li5SVJIlPDn7ChtAN/P3q33jYeJCWn0aHtR3kTnX7xYXSQf57TI4+B1ut7X21S3h0ck+dJnPrX7gMGIDmP5/99QkJRA0ZiiEtjcAli9H6+xc5vuDaNZR29mg8io/Tkn3gIDFjx2LKzETt7o735Mnknz9Pym+/YUy+GfVcaWuLZYUKaPz9kfLzMeZkY8rKJv/cOSSdHFxP4+eHTcOGGBIT0cfHY4iLQ2lri1W1avJXjepoSwWhsr39WvPi6KKjSf3tN3JPnsKibFmsatbAumZNLMqWLbYTLxmN5B49imQ0Yl2v3m1H4IVH65F1qpcvX85rr73GnDlzaNCgAb/++itz587l/PnzBAQEMH78eGJiYvjjjz8AWLhwIUOHDuWnn34yT/9+5513UCqVHD16tEQvRhCeVhdiM5n410Vm9q2Jg5UcS2DflST+iclAbzSRqzOSqzMQ5GpL33r+WGoe4A1pQRb88QrEnABrV3m0wa1cyV7IQ4jMjGTI9iHE5cShVqoxmAyUcijFhlc2mMt8f+x7dEYd9bzq0dincZGAaILwNBHPppIl7udjtn8q7Lwlfk3jsfDy5/d+/MnfYeMYOVJ41zlQtec9H3o+5Ty9N/UG5A71/DbzKed0++dVrj6XGadn0DGoI5Vc5RfGHx/4mA2hG+gQ1IHvmnxXqPycM3P4OeRnJjSeQKfSne79moQnRpIkpIIClJaWdy98G7qICKJGvYkuNLTQdo2PDy5Dh2DTuDEaH59ip3gbUlJIW7yEtCVLMKan39P5FNbWqN1cUbu6oVCpkAwGJKMBJNAGBGBZqSJWlSujtLYm9fffydi0GYzGIvUo7eywadwI22bNsG3aFElvIH31KtJXrsIQF2cuY/fSS9i1bYNl+fLmODySBKbcHEyZmRgzszDl5WJdo0aRFxf3y5Sfjy48HIVajbZ06Rd6Wvwj61QDzJo1i0mTJhEXF0flypX58ccfadq0KQADBgwgPDycPXv2mMvPmDGDOXPmEBYWhqOjIy1btuT777/Hx+fe1sGIB63wPOo55zDHwlOLbPd1suJ/7YNpV9nz/n+J5aXDH50h7gzYekLf5Y9sKviDiMuOY9iOYYRnhgPgZuXGzh47USgUmCQTzZY3I70gHQArtRVNfZvSOqA1TX2bYql+8AetIDwK4tlUssT9fMyijsH8VvL3jgEw6gho7+NFpskIG0ZDyGJAIaeIrD3wng8fv38855LP8WPzHynjVOb+2g6cTz5P7829USvV7Hh1h3lZUURmBB3Xyutxf37pZ5r6Nr3vuoVnlzE7m9gPx5G9axcaf39chw/HoXMnFLcJjvxfprw8MjZuRB8Ti8bLC42XJ2pPL4wpyeSdOUNuSAj5Z87ec8f7v2waNcKha1d04eHknTpF3pkzmHJuScGqUMjLL250vlUODigsLG6uKb8XSiW2zZrh1LsXNo0bm0fBJb0eY3o6+sREDImJGBISMaalYsrNw5Sfj5SfhyEpmYLr19FHRZkDz6nd3bFp3Bjbxo3Q+Ppiys7GmJWNKTtbXo/u7Y3G2wuVszMYjRjT0zGmpWHMyACFQo48r1ajtLJG63/nSPSmvDyytm8nY/16DElJWJQth0VwBSwrBKP180VpZ4fS1haFVvvYOvqPtFP9uIkHrfC8MZkkftp1lYiUXKy1Kmws1KiVCtaciiE+M5/KPvZseLMxygcJbJabCgs7QOIFOX9pk/eh6fuPLRXX3RhMBiIyI3CzdiuUG9tgMrAnag8nE06yO2o3Mdkx5n1OFk4MrzacfsH9nkCLBaF44tlUssT9fMyMephcGvIziGq/iGVp5fBxtKZvvaJTb2/LZIK/PoTjN4KLtZkADd58NO0tRv8t/TmTdIZR1UYxsvpIAEbvGs2eqD009G7InJfnlMgH76TcJGw0NmL21DNCkiR016+jDQh4oFRi98KUk4MhORlDUhKG5GT5/4JKjUKjRjIaKbh6lfzzF8g/fx5DYiJ2L72Ey7BhWFWpXLitRiP5//xD1t69ZO/dK0czB6xq1cKpdy/sWrdGodGQd/o0mX9tJWvHDoypqXLn+8aX0toalZ0dSnt7kCTyz50z1692d0dhYYExPR1TVtZ9XaPS3h6poADpluDRd/JvILo7lrG2xqpqVayqV8OyQgWQJEwFBUgFOvLPnSNzy5bCLxluR6NBZWOD0tb2xpcNmCSk/HxMugKk/AJsmjTG6/P7mH1zG6JTLQjPoFydgTl7r9O4jCt1S8npTGLS81h9MpquNXzwc77HB3puKmx6By6sl3/2rCpPz3uK1lnfiSRJXEi5wLbwbWwN30pcTlyhD016ox6VUiXSqwhPlHg2lSxxP5+A6JOQm8LqrIq8t/IM1f0cWfdmo/urQ5Lg7y/g4DT555e/hMbvFC2XlQCXNkLSZUi6JP9pYQevr5ezVzyALde3MG7/OFytXNnefTvH448z/O/hqBVqVndeTZBj0I0mPnhKx/CMcHps7EEZxzIs6bDkhZ4GKzwYyWi858Bn+oREMOjR3ONs3uIUXA8jffky0teuw5T5n6wxCgUqVxc07h6o3d1Ru7qgtLZGYWmF0soSlYMD2lJBWJQpjcrFBUmnI/fECXL2HyDn0CHz6LTS1haljQ2mzEz0cXEYkpLMI9sgj7CrHB3l69frkQwGjNnZSLm5d22/xtcXh66vYFmxIgVXr1Fw6SL5Fy9hSEqSO9z32HW1fekl/H6eefeCdyE61YLwnJix8ypTdlwBoHl5N95sUYY6gXfIH/ovSZLTnmx5H/LSQKGUc1sHd4YKHcDOS/5gE3VUjipuYQ/1hoNzqUd8RffHYDLwV9hfNPVtak7HtfTSUmaenkkVtypUc61Gt7Ld8LDxeMItFV404tlUssT9fHJi0/No+N0uVEoFIZ+1ws7yPmc2SRLs+wF2fyP/3P4HqHsz/RUxp2BJTzmw5n/51oWBWx5oNpXeqKfN6jYk5SXxXZPvmHt2LqEZofQP7s+4uuPI1mXzy9lfiMqKYlqLaQCYJBOZBZmF8mTfyTdHvmH55eXFrt0WhKeZKT+fvNOnUVhYoHJ0lL/s7R/J6L2k02FISkJhKXfMizuHZDRSEBpK3ukQ8kJC0IWFodBoUFhYoLCwQO3ign2HDljXqY3iNhkIJJNJnq6enXVjGnoWpuwcTDnZoFCitLRAYWGJ0tIClbNzsUHv7pfoVAvCc2LHhQQWHAzj8PUU88u5OoFOjGhWmnIedoVGr0OTsglwtkatuuWXUVYCbHoXLm8uXLHWFnTZhbcp1VCtDzR576nrXN/q3wA1/3KycOKHZj9Q16vuE2yV8KIRz6aSJe7nk9Vs8m4iUnL5bUBtWlZ4wJeUu76BfZPl77v+AtV6w+WtsGog6HPBtRyUbwduFcDGXc51XZABDd6CNt/erCctQn4hrLaAusMhsLE81bUYm65vwlJlSWx2LJNPTMbJwomNXTfiYOFAWEYY3dZ3wyAZmP3ybGw0Nnx/7HusNdbMbz3/rqPOOfocXlr5Ejn6HH5p9QsNvRs+2H0RBOGZJTrVgvCcCU/O4Zd911l9Mhqd0QTASxXcmT+gDiDnxa4/YScqpYJXavjQraYPFTxv+f+SFg4XN8pfUTci72tswLcW+NaB2BAI3SlvV6qh5uvw0mdyiq6njN6k50raFc4mnWXN1TVcSr2EUqFkbK2xvF7xdfMHJaPJSLY+2zzCLQglSTybSpa4n0/WR6vPsux4FEMal+KTjhUfrBJJgq0fwdE58uyomm/Aqd9BMnHdoT4jdGNYMLwlPo5WcvmLm2D5jVgZvZfIs6iubIc1QyE//Wa9nlWg/ptQuTuoi08r9O+I8qf1P6Vn+ZuRyCcfn8wfF/7ARmNDjl5eq2mjsWFlx5X42fuRkpfCiisrGFJ5CJr/jJYvv7Scb45+Q6B9IOtfWY9SoURv1BcpJwjC80t0qgXhORWfkc+8/ddZFxJDRW8H/hgkj85eScii1y+HScu9GSSigqcdXar70KmaF75Ot6zHzoqXc1y7lgfVLVN0oo7Bnu9udq5t3KHd91Cp621HCZ60fEM+Xx/52jxyPbTKUMbUHAPAoG2DOB5/HA9rD6q5VaOqW1Vqe9amksuzsbZceLqJZ1PJEvfzyVofEsPby0Io72HH5jGNC894AowmiaPXUwj2ssfJ5g75ck0m2DgaTv9p3pRRoRe1QjqgUGnY/X7zws+jbR/D4Zlg4QDV+8LR2fJ275rgVQ3OLANDnrwtuDP0WlTklJfjs/h+6yXa1dbRrWJ9VMqb61czdZl0WtuJ1PxUFCjoVrYbb9V4C1crVyRJYszuMeyJ2kMll0rMaz3PnNtakiS6ru9KaEYoH9X9iJf8X2LqyalEZ0WzuP1isbZaEF4QolMtCC8gncHE7suJrDkVza5LieiNN/97f9WlEq83CLy3isIPwMZ3IOWq/HO5tvIaOUe/Em9zSZAkieWXlzP7zGz+bPcnfvZyO/8dufivel71eLvG21Rxq/K4myo8R8SzqWSJ+/lkJWbl03DiLgwmiZ61fZn0ajXzvmNhqXy58TznYzMJcrVhw+jG2FrcYV2myQjrRsI/q6DZh4yIfJmtFxJoW8mTOa/VKlzWqIcF7SH62M1tdYbK08HVFnLgzRO/yVPLkWDMaXAOunm4SaLhdzvJ0xmZ9GpV2lb2KtKckwkn2XJ9Cz3K96CCc4VC+/6O+JsvDn9BRkEGrQJaMaXZFBQKBcfjjzNo2yCs1Fbs7LETvUlP29VtyTPkMeulWTTxbXJf9/deSJLElBNTMGHig9ofiI67IDwFRKdaEF5w6bk6tp6LZ31ILEfCUtg8ugkVve/j/4+hAPZPhf1TwKSXA5l1mAJVejy1o9a5+txCKU+yddlISFxKvcSZpDOcSTzDgdgDGEwGPG082dJtCxqlmMYnPBjxbCpZ4n4+eRvOxPLVxvMsGlyPYC97EjLz+WrjBTb/E1eoXOdq3kzvXb1Qp89gNHE5IYsKnvao/k0HWZDNmUQDXX4+iEIB299pSlkPu6InzoiGuS2hIAs6/QRVexQts6grhO6Cph9Ay0/MmxcfjeDjteews1Sz5/3muNha3Pd1n006y4CtA9Cb9Lxb610GVR7E2D1j2RGxgx7levBZg8+Am1PJq7lVY1G7RRhMBtRKdYl1fjeGbuR/B/4HiBzbgvC0EJ1qQRDMErPycbO1MD/4Z+8JpYKnHQ3LuGChvkuah8RLsOEtOUI4QKVu0HHqU7nW+l7EZMcw58wcarrXpGvZroCcg/S7Y99R27M2thpbrqZf5UraFULTQ/G28ebdWu9S3b36k2248NQRz6aSJe7n0yFfb8RSIz8XErPyafnDXnJ1BnrX9adFeXdG/nkSg0niu25V6F3X31zurcWnORaeSssK7szqV9Ncx2vzj7L/ajLdavowvl0wuy8l4utkRcMyrv85cYb8p+VtYmCcWy0HNrP3gXf+AaWK9FwdLX7YQ1quns87VWRgowcPsLni8gq+PvI1SoWSX1r9gru1O8svLad7ue6UcyoHyM+KtqvbojPpaOnXkuMJx5nXeh4VXR5wDfp/7Inaw+hdowGo7ladP9r9IUarBeEJE51qQRCKdSoyje6zDyFJoFUpqeRjTy1/J1oGu9OwtGvxBxkNcGCqvN5aMsofauoNv7nmzdJeHtlOugTx5yAvFSq/CvZFp+E9jf7NdXo7c1vPpb5X/cfYIuFZIJ5NJUvcz6fTprOxlHazJdhL/jv5dV8oOy4k8FOfGng5WHEqMo2Rf54kIbPAfEzjMq78+notzkZn0PvXI6iVCna915zVp6KZvvNq8dPA70afD1PKywHM+q+GMi/z+fpz/H44gnIetqwZ1YizUelo1UpqBzrLQdP+2yHNSYawfXB9D0gmOWaI1gaQp15/evBT1oeux8nCieUdl+NlW/QZNuHoBJZeWmr+eVjVYYyuMRq9Uc/yy8vpVaHXQ82ACs8Ip/O6zkhILGizgNqetR+4LkEQHt69PptKPlGZIAhPtTLutrzRIJANZ2JJzdFxOjKd05HpzDsQRu86fnzasSI2/10rp1JDsw+h9EtyVNbUUNjx2c399j6QnQAmw81t+36AdpOgas+ndrr4vyq6VOSt6m9xMuEkBcYCyjqVpZxTOUo5lOJy6mXqedYzl/34wMck5CRQybUSFV0qokBBekE66QXp5BnyGFltJFrVHYL4CIIgPEM6VvUu9PPQJkEMalQKtUrJhjOxvLciBL1Rooy7LcObBvH5hvMcD0/lcnwWP2y7DEDvun74u1jzcrAH03deZf/VJAoMxrvPlLqVxlJ+nhz7FU4v5qJNXRYdiQDgiw7lObpxPldC9tPQLhEs4iA9Cizs5FlV1s5g0EHi+cJ1WrtAqy8BUCgUfFL/E66kXSFTl0mWPgsvinaqh1cdTmx2LC5WLnQo1YFaHrWQJInRu0dzMOYg8TnxvF/n/fu4w4UFOgTSo1wPVlxZwdx/5opOtSA8I8RItSC8oCRJIjI1l1ORaRy8lsLqU9FIEkx+tSo9at8hIJkuB04skNNyxYZARuTNfZYO4FFFHklIOCdvK98eOk4DO49/Tyz/+ZR3tG+n5YqWJOUlFbuvtENp1r2yzvzzwZiDpOanUmAsIN+Qj6XaktaBrbHXit9jzwPxbCpZ4n4+ey7FZ9L150M0L+/G5B7VsLVQcyoyjYw8Pc3LuXHgWjIzdl5jRt8aeNhbYjJJ1J+4k8SsAv4YVJem5dwA+Xk0a08oGXl6RjYrffvo4rEh8GszJJWWwa6L2RWhp0Nld37WzoQL6+6t0e6VwKMi/LMSlBoYdRhcy5p3x+fEY6m0wNHCAZTKO1RU2N8Rf/PunncBmNJsCq0DW9/TcQaTgZF/j6R9qfZ0KdMFpUJJVFYUndZ2wigZWdZhGZVcRcYKQXhSxPRvQRDuy6HQZDaeiWVC1yrmNVxno9Op4GmPVn2HDxY5KZB8BRx85S+FQp4ufnCaPF3cpAeVVv4y6uRIrw6+0G0uBDR4PBdXQiRJ4kraFf5J/odzyee4knYFtVKNg4UDThZONPRpSNvAtoCc6qvp8qbk/ZsK5gYbjQ29y/fmtYqv4WLl8iQuQygh4tlUssT9fDZdTciijLvtPa/9/Tcf9oCGgXzRWe4srjkVzdgVZwBwsdHy1ztNcLezLHqwJMGcJpDwDwfLjePt63XZXXkrdiFzkZQaVpqac1bvyxtd2lG2QhXQ5crpI/NS5YjkfvXA1k2uZ0lPuLodglrAa2tvvuhNC4dl/eQXw0o1qCzkKORVe0G774pvU/QJ8KrKlJCZLDy/EGu1Na0CWqFSqnC3dufN6m8Wey8ydZn89s9vzD83H3utPZu7bsbR0hGA8fvHk6nL5B2HapR1rwb+9YqtQxCER0t0qgVBeChpOTpqfrMDG62ahqVdaFrOjYalXSjlanPvgVMSzstpVeLOFN2ntoRXF0CF9iXb8KfE9YzrfHPkG1QKFZZqSyxVllxLv8a19GsA9Crfi0/qyxFs90fvZ921dViqLfGz88PPzg9/O3/USjUp+SlUd6tuzp0qPD3Es6lkifv5YthxIYGhf5zAz9mKfR+0ICGzgFY/7iUr34CTtYaGpV35uV/N21dwZA5sHQeeVTFU7oH670/l7d3nM/R0IDsuJDCubQVGNi9954akhMKs+vLL3p5/QMUu8rbfO0FmTPHHDN4BfnULbzs0E7Z/DAGNMPRbyZBdb3Iy4aR5939nMC3aPBR94kUOepTiVPI5DJK8bOqLBl/QvVx3czmDyYD66t+wtBdobeG9S/J0dkEQHiuxploQhIcSlpKDs7WWlBwd2y8ksP1CAgCe9pbUDnTi3VblKO0md/TiM/KZteca/2sfbI74CoBHJRi6B9LCQKEElQZQwOaxcGUrLO8HnaZDzdchKx7OLpfzmhr18ih2QCP56xkJeHarIIcgfmvzW6FtJsnEnqg9/HbuN96o9IZ5e1RWFNsjtt+2rkXtFpmjj6fnp8uddHUxoziCIAhPuUZlXNCqlUSl5nElIZsJWy6SlW+gmq8Dy4Y1QGc0mctm5es5H5tJ/SB5Vk+ezoi+zCvYKz+B+LOo48/KBVt9DVVepUFGGDsuJHDkesrdO9UupaHR27BvMmz9Hzj4wdLecnwQ1/LQewlorcGQL8+6Orsc/v4CBmy+OaqdFQ97JsrfRxxEvf4tfu48g83hf5Gpy0SSJBwsbkYzz7q8halJhzEoFZAUAkAph1J0Kd3FnI3iX2qjQX55AESb8nD9ZwWWtQeb959JOkMZxzLYaORAawXGAixUN9OJXUq9RGp+Kg29G972FmQUZJBnyMPTxvPO90oQhLsSI9WCINyWySRxPjaTPZcTORSawsmINPMHnik9qtG9li8mk0TXWQc5E51BrQAn5r1e+/br4f5lNMDGtyHkT/ln3zoQc1KOxloclzJQqqn8FdgEbG4TpfwZdSXtCifiT5CtzyYqK4rIzEiis6IxSkZcrFz4pP4n1HCvgSRJjNo5ipjsGCY2nkgl10oUGAuIy44jNT+VCs4VCuXpFh4t8WwqWeJ+vjgGLDjG3itJ1Alw5lh4Klq1ki1jGlPG/eZIrNEkMeT34+y7msznnSryeoNAPlh5hsPXU9jkMRfH8C1ywXojoO13oFBwITaT9j/tx1qr4sznrdGo7rImWpcLP9eTY4MolPIzyL0SvL5enib+r4xo+KkmGAug70ood2O99NqRcGYJOAfJgdFMemg4Glp/U/Rcuakkz2nIr+p84tQq6po0NOu3GX+n23T+902GXd9gAnp7e5KuteSdphNpV6oda6+t5esjX9PYpzHTW0wnS5fFwG0D6RjUkUGVB7E3ai8f7PsABQoWtV9EOadyXEm7wq7IXQypMoSQxBBWXlnJjogd6E16hlQZwts13773v0BBeIGIkWpBEB6aUqmgiq8DVXwdGP1SWfL1Rk5GpHEqIo1AVxtzmY/aBTNs0QlORsjpuhYOrIu/yx06dyo1dJkJtu5yqq5/c2D71YfqfeWIrBGHIOIAxP8DKdfkrxM3Rn6tnOWOtY2b/FW5GwR3fmaDn5VzKmfOg3on8TnxXE69TFJeEv239MfBwoGU/BTz/t7le/Nx/Y/NP0uSRFhGGIfjDhOVFUVN95o08mlkHtkQBEF4Ej7rWBEHKw1vLwsB4P3W5Qp1qEHuVDvbWGA0SXy2/jxbz8VzKDQFpQKimw7AMWIbVOoKbSaYf/dX8LTDyVpDWq6eszde9IKce/t6Ug7lPGxR39rR1lpD2wmwvL/cofaqBq+tk6OF38rBF+oNg0MzYOeXUOZliD0ld6hBjhGSel3OjnFoBtj7Qv0RN4+XJNj0Dq6ZcfzPpQzkZkN2HMSEQHGd6oxo2D8VgNgWH5J67U8SFCbG7R/HzJCZRGVFAWChskBv0rMtfBtX067y48kfOR5/nEOxhzBJJup51cPTxhO9Uc+4feO4ln6NP87/QZY+y3wqBQrqed1cr30x5SKp+aloVVosVBYEOQSJ5UeCcA/ESLUgCCXiSkIWAxccJyY9DxcbLe+3Kc8r1X2w0t4lZcrZlXJgmMrd5Ol4/5WXDpGH5dyiYftuRhX/L+8a8NLnULrFw17KUy09P52vj3xdaLq4tdoajUrDwjYLKeNUBoAjcUf4+MDHJOYmFjpeo9RQ17MuM1rOQKN68FyqAHmGPKKyokjKTaKya+VC0xxfBOLZVLLE/XzxGIwm1pyOoXtNX1TKoi9FJUli9t5QJm29bN72XqtyjH6prDzKrLEq8jJ1x4UEvBwsCfayR6VUkJaj47XfjnIuJrNQcLRbTgK7vobMWGj7HflqO3r+cpg6gc582rHizXK5qTC9GhRkQtdf4Ogvcse6Wl/oOlsus3+q3OlGAXWGQK0B4FkZQpbCuhFy4LPBO+TlT3u/h4DGMHBz0RuzcgCcXwv+DWDgX+Qt7c2ihEPMc3EhTzICMKr6KEZUHWGOcbLw3EKmnJxirqJb2W58Uv8Tc87sTdc38c2Rb8jR52CltqJ9qfb0KN8DF0sXPKw9zPV8sPcDtoZvNddjr7Xnuybf0cS3yZ3+KgXhuSUClQmC8NglZOYzaOFxzsdmAuBgpWHbO03xdLj9+t9/fwXdc/CzvDTIjIPcZMhJkkeyj/4K+hx5f6lm0PHH4jvoz4l/o5AbJSM+tj7mFF233sPx+8ez6fomtEotNT1qEmAfwJG4I0RkRlDZpTJLOy41l118cTEKFERlRRGWGUZERgSOFo68Wu5V2ge1x0ptBcgRzXdE7GBz2Gaupl0t1GG319qztMNS/O39H9NdePLEs6lkifsp3M7Wc3F8tOYfGgS5MLNvzWI74MVJzi6g/7yjXIqXR2bnvV6blyt6mPcXlyt75Ykoft59jf71AxjSJKhwhft+kDvgaisw5IHWDkafALsba5IlCbZ8AMfn3jzGpzYkXQZdFrT8FJq+DxkxMK0KSEYYdQTcg2+WD9snB0tTKGHYXvCqCpc2w7K+JNu5sbT5aKp51KCpb9Mi17v+2nrmnJlDr/K9eKPSG0Weq/E58YQkhdDYu/FtR5+nnpzK4djDFBgLyCjIIDU/FQUKRlYbyfBqw1Eq7jylXmfUEZ8TX+hZkJSbhJu12x2OEoSnl+hUC4LwROTqDCw+EskfR8JxtrFg/ZuNzPte/+0YKgXYWmpIyMwnMTOf+Mx87C019Knrz7ut7j4FuljZSbD/Bzg+X17TZuUEvRZDYKO7H/sckiSJpZeWEugQSE33moWCmoVlhJFRkGEOfJaty6bJsibmCLT/1b5Ue75v+j0Ab+58k33R+wrtt9PaYaW2wtfWl4VtF6JQKMgz5BGZGUl8TjxxOXHkGnJpE9gGH1ufIvWn56dzJe0KOpMOvVGP3qQnR59DekE6AfYBtPRvedvrPBJ3hNkhs2lbqi19KvS539v00MSzqWSJ+yncidEkoVTc+wvYxMx8+s47yrXEbNztLFg0uB6l3WxQq5ScjU7nm80X8XOyZkrPauZjJEmi08wDnIvJ5MO25RnVvEzhSnU58FMNOZgZQKuv5GBnt5IkuL4bTi6UO8OmG79b/erDwC2gvNGJX9YPLm2CusOg/WR5W04yLGgnp6msPRg6Tr1x8XqYWhFyEuVnW3DH+7hzD05n1PH9se9ZcWUFAP2D+zOurhw87XLqZRJzE8kx5JClyyJbl01sdixbw7fiauXKms5rUCgU5OpzabWqFWUcyzCo8iCa+jYt9u8wJjuGhJwEqrpVRa18ulan3vfLf+G5ItZUC4LwRFhr1QxtGsSgxqVIzi4wbzeaJA5dS8ZgKvoeL19fwNXEm2u8CgxGotPyKOVig/JeRiRs3aDd91B/JKwaJAc9+6MLdPkZqvWSy+jzIWwvZCdChQ5F18w9RxQKBX2D+xa7r5RDqUI/5xny6FKmCyn5KQTYBRDoEEiAfQDnk8+z7PKyQhFp2wa2JTQ9lFfKvEID7wYE2AXgaOmIwWQgvSDd/IFjX/Q+3t/7fqHzzDg1g85lOjOk8hD87P0AuJp2ld6beqMz6Ypta9vAtuZOtSRJnE0+SzW3amTpsphyYgqrr65GqVDybq13iz0+35DPqYRTHI47zKHYQ0xvMR1fO997uIOCIDxt7nV0GmDFiSg+XCVHBvdysGTJ0PqUcr0ZS8IkwbGwVE5FpDG2dTl8HOXZOKci0zkXk4lWraR3HX9ydQaOXE+hZYUbo9taG2g2Ts5g4Vwa6o0senKFAkq3lL+yE+H0n5B4AV7+4maHGuTp4Zc2yVPDX/pc7jD/2V1em23jBi0/ueXiNVC9DxycDqcXPbZOtTY3lU+rDKeqW1Wmn5pO3wo3nyu/nP2FHRE7ij9OqSU5Lxk3azdCEkPINeRyKvEUp3ad4iX/l/iiwRfmfNwA55LPMXDrQPKN+ThbOvOy/8u0CWxDLY9aqJR3WUL2GHx5+Esupl7kh2Y/4Gfn96SbIzylxEi1IAiPhcFo4sj1VKLTcskuMOBub4mHnQUe9pZcjMvE18maKr7ymtxTkWl0m3UIG62KYC97WlRw5/UGAdhZ3sMaYF0urB0OFzfIP9cZIn+wubbz5hRxjTXU6C93wp2Dbl/XC85oMqJUKM2dZYPJgFKhvOv0v7NJZ3lz55t42XjhZeNFlj6L4/FyMDqVQsXc1nOp41kHk2Siy7ouGEwG7LR2aJQaNCoNlmpLnCycqO5WnV4V5Jci28K38f7e92ni00QeIcmTp5639GvJ5GaT0arkiPM7I3cSmh7KsbhjnE48XajD/lmDz+hRrkeJ3R/xbCpZ4n4KJeWtJafYdDYOjUrBzrHNiw2c2XfuEQ6FpjCwUSCfd5LXWb+97DTrQ2J5tZYvH7cPpt30/SRlF/DX200o53EjkJokwYX14FMLHB+ig2Uywc915CCc/2fvvsOjKr4Gjn9303vvgRRa6L33IlIVG4oKIvaO7VV/FuwodlRQEUHBggU7gkjvvZcESEJCeu/Jtvv+MWSTJQGSEATkfJ5nH8zduXfvDpi5Z8qZHnerNdQlWeDdFG5dDP4tbMtnxanyOj08erBuW00WpMCeb6HVKAhqc/by1R1doUbTLSboehvlfR7E2aeqU/a9He+xKXUTrg6ueDh44OHogaeTJ31C+9AntI/NaHNGSQYLDy1k4aGFmCwmAl0Cea3/a/QK6QWA0WLk9qW3cyjnkM3v7CYeTXih9wvWcvVltBita8rPRVxeHHf9fRcWzcJ7g96jW3C3c76muHTI9G8hxCXrz71pPP7DbsqNVVtseTrbc0e/aCb3jcTL5SyNpMUCK15UvfrVeYSCsxdkHVI/6/QQM0ZN3wuvpZEsyQZHd3CQPaHP1e7M3Xy691MSChL4/ZrfrQ86eeV5eDt5n3Va3ef7PuejXR9hPpmkp6lHU17q85LNw018fjw3/H6DzUNZkGsQfUL70Du0N71DetuMjpwraZsal9SnaCyJ2SUs2p7MxF4RhJ4chT7VuiNZTJy7FRcHOzY8PQSTxULfN1ZiNGv88VA/2oV5cc+C7Sw7kEH/Fv58NaVH40//3fQxLPtf1c/B7eGWH6vWaJ9q7pWQvBm6TIL248ErDDzDwN6pZlmLBT4fqpKpAbQcAX2nQkTvs99X3N+w6BYwV5tFZOcE3aZA/8dttxurh4M5B3lq7VMkFiYCMGvoLGsCtIKKAlztXdmWvo1lx5exImkFBRUFALzR/w1GR48G1Kyl9JJ09ufsJ6s0i0JDIYWGQvLK8+gT2oexzcYCkFuey41/3MhNrW5iYpuJ1o7X2hQZikgvSSfaK9o6Mp5TloOfi9ofPRaDZnkAAQAASURBVKMkg0dWPcKBnAPY6+15odcLXNPiGkqNpTjYOVjbs8o16D5OPo3a1ogLS4JqIcQlzWS2EJ9dwvbEPOauj+dYlhpl9nC2Z+nUAdbpeme0ayHsOrm2OmY0hHRSx+NXw6aP4Og/VWWb9oG+D4NPFBz+HQ79AWm7wdkbhj4PXW+3nbonGqSgoqDBWcKP5B3hkz2fEOEZwd0d7rZZKw5q2vms3bMIcg2iV2gveoX0ItIz8rytg5O2qXFJfYp/U/X1048MbYFep+O9f+Lo0tSbxferfBzHc0q44t21GMwWpg5TI8eJ2SW8fUNH2625GqosD95prZKeRQ+C8QvA+Qz/9ncthF8fsD2ms4NBz8DAJ2sva+eo1mRz8nG/aW+46iPwP2W9eKXDf8L3t6n8JDFj1GyvNTMgaaN636sJTFmmAvoGKDOV8da2t/gh7gcmtZnEk92frLVcibGED3Z+wNoTa1l81WJcHVxJLU5l4pKJ1plKp7oy8kreHvg2oDpiP9ipOtabeDTh8W6PM7jJ4BozrX4+8jNvbXuLImMRHg4edAvuRqfATszfP5+bW9/MvR3vtd73c+ufs+684evsS255LgtGLrDmKPn5yM+8sPEFALoFdePuDnfTK6TXadugIkMRy48v53jhcVr7tqZDQAdC3EJsyn97+Fu+OfQNPUN60ju0Nz2Ce1BQUcC6lHVsS9/GjAEzrLMCqo/MV5grSCxIJL0knVD3UCI9I895x4/LlQTVQoj/DLNFY8m+ND5ceYRSg5l1/zfY2uisPJxB+zBvAjxq6ak/m8xDsPEj2LtIPUCcSUhHGPU2NOnRgG8g/oukbWpcUp/i3/bn3jQe+GYn3q4ORPi5sSc5nw9u6sTVnaoCxul/HeLTNfE25615chARfm6nXq5hDv+p2qI+D4P96UdTATAZ1JZd6fugMEXtZ20qV++NXwBtrlL/XV4IH3ZV67SveFkFxxtnwu5v1OizkxdcPxdaXFF1bU2Dvd/Dr/erKd9txsF1n6v13JXJ1/58XK35DmgNU/5SSUEbaN2JdezP2c89Hc6cUbzUWIqrg5q+b7KY6PNtHwxmAy19WhLuEY6noyeeTp54OnrS3r+9dc9ti2bh92O/88HOD8gqywIg2C2YEZEjGBE1gja+bdDpdMzbP493d7yLvd4ek8U2YWc7v3bMHzkfJzsn6zU/2fMJs/fMtpapPor+Q9wPvLf9PZt9wNv7t2dKuylEekZat7wsqCjgtc2vsTJ5JRXmCqrzc/ZjWu9pDG6qtgeNy4vjut+us76vQ4dGVej25Ygv6RLUBYA3t77JptRNGC1GThSfwKJVzfbzcPBg/YT11rqetnEaJ4pOoKHh5eiFl5N6eTh64OXkZbNU6nDuYfQ6PQEuAeSU5bAvex8Hcg5wKPcQQa5BvDvoXWtZs8Vcda863Wn/bo8XHmdpwlJa+7Wmc2BnPBw9ai0HqrOgsKIQJzsn3Bzc/vWEcRJUCyH+cywWjeO5pdaEMwaThR6v/4PZovH4FS25tVdEw0YPClNhyyewfR4YT44YtB4LLa+Eg7/Bylfh5DQ0IvtDk54quA7vXveEZxYzlBeohxDJIPqfIG1T45L6FP82s0Vj6DurScwp5ZVx7Yjyc6NHlC+O9lXtSFG5kUe+201xuYkofzeiAty4rks4AR5OmC0aBWVGfN1UMPzbnlSuaB2Ei6MdZQYzG45m22zh1eg0Df5+Ts28cvKEe9aoPCF/PwcbPwS/5nDfpqpgvSAFfrwdkrcAOhj6ggrmD/4C69+HjH2qXLvr1V7cdqfkM85PgrnDoShNZTOf9IvaK7y+TBVqplhxBnSeqAL3OorNjaWpZ1PrVo9nU2os5fN9n/PN4W8oOZlXxcPBg9U3rsbRzhGjxciS+CWMihpFbF4sm9M2szVtK+6O7rzU56Vag73EgkRKjCU08Wxi3dKyuvSSdOYfmM+PcT9ag+ZQt1CWXb8MUMH5iJ9GkFaSRrRXNJ0DO3Mo9xBxuXGYNBOjo0fzRv83ABWkrkpexZa0LWxO20xiYSJ2Ojs6BXZiQPgARkWNItgtGE3TGPHTCFJLUq334eHgQYh7CKnFqUR4RvDdmO+s713z6zUczT9aa52FuYex9Lqqvcpv/vNm9mXvq7Vsl8AufDnyS+vPPb/uSamp1PpzsFswkZ6RRHpGMjJqpLUDILkomVGLRwGg1+lp5dOKpp5NySjJIKU4hQc7P8i1La4FVJ6WW5bcAqi8LB6OHng6euJi74KTnRPXt7zemlS1zFTGP8f/oYVPC2J8Y2q95/qSoFoI8Z+XXlDOnV9tY3+K2hc7JtiDp0bEkJxXSnJuKc+OrkrMsispjyh/N7xdzzASYDKofUNPfUgozlJrtHctrHmOkye4+qmXW4BaE9ekh1qj7eSl1r8d+FkltinOUGVCOqpXZH9oNrgRakJcCNI2NS6pT3EhLDuQTrnRzOj2IfXqlM0vNfDId7vJKqpg8f19+HHHCZ77ZT9dI3z44rbuTJq3lb0n8pl1cxdGtg9B0zTWHslmQAv/xh1pMxth/hjV1gR3gHGz4LPBavbVzT9Ay+G25U0Vai/tnScDIRcfNQ0dwMENet0Lg589/XKnjAPwxUjV0dxqlBohPzX4rvU+Teoe936vgvjykx3VrUbDDfNqXxfeiCrMFaw7sY6/Ev7Cx9mH53o9d/aTzlF2WTYLDi7g78S/8XXx5etRX1vfW5O8Bn8Xf9r4tbH+eygzlXEo5xB6nd46pfxUWaVZONs71xrs55fnsy1jG56OnkR7RePvov6taZpGkbHIpgNgdfJqykxlgBo5z6/Ip6CigGJjMT5OPjzW7TFr2bv/vptDuYfIr8jHxd6F1r6tae/fnhi/GELdQq2BMtQMqqsb3GQwM4fMtP784sYX2Za+jaSipBpl72p/Fw93eRiwDapr81Dnh7i7w92A6nS5/vfrGdZ0GO8Nfu+059SHBNVCiMuC2aLx7dYk3loWS0FZ1RRuvQ62P3cFvm6OmMwW+s9YRV6pgcGtAnGw02M0WzCYLAR6OnPfwGa1ZoetIfsoJK6DE9sgeSvkHDlzeSevqhHu0+l5H1z5Ws0HmNJcdcy5YeuPxfknbVPjkvoUl5LU/DLGfLie3BID3SN92HE8D4sGDw9twaPDWvD8r/tZuDkJJ3s979/YiW+3JbM2LstmernZotVrqzBN01gdl0WUnxuR1bYIoyAFPu0PpTkqMDaWQIvhcMsPp7sQbJ8Lfz2lpnq7+ELPe6HHXXWbfZW4ARZcA+YKCGqvtvhqOUJ1FoPacSMvUWU2T9sNqbvUlPXKqeoAHiGqnTNXQPNhcOPCho16i3+NwWzATmd3xm3OCqo98xgtRk4UnSChIIGEwgTKjGU82+vZGudklmayI2MHmaWZBLsFE+4eXmMWgKZplJvLKawotCanKzeVYzAbiPSKtG4XeijnEG9vf5veob25s/2djfK9JagWQlxWcksMzFh6mD/3ptEy2IPhbYK4sXsTvF0dSckv484vt3MorbDWcwe0DOCrKQ1YK11eoEaxS7PVw0xhqtojO3mLWncGKrCOGQ1tr4GmvSD7CKTtUkH53kWqTMwYuHYOOLpCRRGsf0+t9bZ3hhGvQ6dbZMr4RUjapsYl9SkuNRuPZnPr3C1YTj5JT+jRhNevaY9Op8NktnDPgh2sOFyVVMvJXs/zY9pwVadQ3vjrMAVlRj6+uUuN66bkl6GDGpnLf9mVwtRFu3F20PPODZ0Y3aHatlrHVsKCawEN9A5w/+bTJyOrlLoLMg5C23FqD+76OPQH/DhFBcWVXP3UEipj7SOVOHuppVXtx0NkP0hYC99OUInaIvvDhO/Ayb1+9yHEeSZBtRBCVKNpGjuO57EzKQ97vR4Hez32eh1/7U/n/65sRbswNSJcUGrEyUGPs8M5ZvouzlLrz4LbnX5a2/7F8PO96qEkrCt0nABr31LTxKtrPgzGzmxwtlVxfkjb1LikPsWlaO76BF754yBXtg3i45u72EwhLzWYuPHTzexLKaBTE2/evqEjzQPdOZBawFUfbcBs0fjk1q6MaFe1hdbPu07w9E/7GBITyOxbu1qPlxvNDHl7NakFVaO9jwxtwdRhLdDpdMSmFxG/+EVGZn7OliZ30mPK2+c/oVNxJsQtg7ilcGyVGiEHQAde4eATqaakh3ZWL99o0J8yxf74Rvj6BjAUqzXgQe3AzV8F6CGd1Aj4qedYzKrz2sUXAlqdW6ezxQLbPofsWBj0P3Dzq1kmYa36M6JfzXsR/3kSVAshRANM+3U/f+xN4/a+kUzsFYmX63neguL4JvhuQtWaNlAPHle8ArnHYOVrKuh28oQed6v12qFdzrxPaH6SGjEPaqceUmSU+7yQtqlxSX2KS1VWUQX+7o61BrGlBhN7kgvoHuljE3DPWHqYWauPEejhxGvXtGfxzhO8d2MnjueUMvKDtVg0+P6e3vSIUtOxP151lLeWxRLm7cKVbYP5YkMCDwxuxpNXxpCSX0a/N1eiaRBMDun4cm3ncKZf1x4n+6oO4oJSIxuPZXMsq5hjWSUcyypGr9Nx94BoRrYLPrcg3Fiupni7+IB3k/qtkT6xHRZeW7XOurrgDjDkeZWpXLOozui1b6kgGMCvhcp63nosuAer6eyaGXR6tf3Xmb5TaS78fA8cUdtk4dUUblwAoZ3UzxVFaor87pNrob0j1B7hnW89/V7i4j9HgmohhKgns0Vj5AdricsoBsDN0Y6xHUPp29yfXtF+Ddu2qy5yjsE3N6pe/4H/p4LnymytWbHwy/2Qst32HK8mKilaYBsIaqMeBhLXqmzlaburynmGq2zmTXqoh42KIjUigA4CY1Tg7dusbolmhA1pmxqX1Ke4nJQbzYz6YB3x2SXWYw8PbcFjV7Tkfz/v45stSbQP8+LXB/qSV2pgwIxVlBjM1jXZ645k0beZP/qTa7Lv/HI7jvY6Wgd78v6KI5gtGj2jfPl0Yldrgs5rZm1gV1J+rffTsYk339zZEzcne/JKDGyKz+FAagEtAj24ulPo+R/1LkqHxPVQkq2WVBWlwYFfwXBye6omPVUQXJnLxNFDdTibDae/pk8UdLgROowHv2a27yVtUZnQC1PUUiu3AChIVv895n3wbwk/3QF5CSpAd3SHipNLyHR24N9CdSC4+KgR85hRKnHb2eqpNFd9ZkBraXcvERJUCyFEA5jMFv7cl8bs1cc4nF5k8961ncN498ZOgFrD/dC3O4kJ9qR/C396Rvnh4ngOU8bNJkCrfWsRixn2fKeSpKXshOw4VfZ0dHrVYOccOfMDRyU7JxV0X/EyhNVc3/efYTZB2h4I73r2snUgbVPjkvoUl5utCbmM/3QTAP2a+/P5bd1wdrAjq6iCwW+vprjCxLvjOzKuUxi/701l6f50Pr65izWQrq560rO1cVnc//VOiitMdAhXgblOp+ObLUl8sSGBDuFeNAtwPzkVvZDP18XTO9qPuZO7W8+f9MVW67Wv6hjKm9d1OLc2riFKcmDDe7B1TlWSM2dv6P0g9FTZnon7Gw79CkdXqjJ6e5Xk02xQHcmVgtqr9doWsxrJTtuj3vdrDjd8qaarL76ratRap1cj457hcN0cNRX94K+wY77KYl6biL4q8Who56pjBSlqqvrxDWqqe+ZBddzFF1qNVDlXogernCrioiRBtRBCnANN01h/NJtVh7PYFJ/DobRCHhrSnMeHtwLUdl69pq+wlne019Mtwocwbxd83BwZ3CqQ3s1qWZt1UrnRjMmi4e7UgJ7q8kL1QJBxQDXQmQchN0FlXm09ViU+cw8AQykkbYL4VSoZjYMLOHmoHndzBWQeUserr4PrfoeaaufiXfNzK4rg8BKI/VNlj/UMVRlcPcNUMO4bXbOXXtPUlD5nrws7DV3T4I9HYedXcNWH0Pn023PUlbRNjUvqU1yOftieTFxGEY9e0RJXx6r2YNbqo8xYGkuwpzOrnhhU74A2Nr2IO7/axitXt2NQq0BABd56HTVGnTOLyqkwWmjiqwK7/FIDt3y+hWYB7izZl4bJotE21JPPJnUjzPv0GboLyox4Ots3/qh2YSpsnq3WWne9HZzr8Puhohhil6iEoMdWqgD5VO2ug7EfqHYR1PrqNW/AmjfVz22vgTHvqdHo6nIT1DKrsjz1yo6D7V9UBf5tTyaMS94GhSdqfm5lhvZKOjs1Zd43Wr28m4JboBo9d/NXiVBTd0LqbkjbC+6BagS+3XW1rwE/lcWscrUUpqnR9sA24HEe90//j5GgWgghGlFuiQGLpuHvrqaAlxvN/LYnlR2Jeaw9kkVateQxAM+MjOGegWq6mdFsobjchI+bI8m5pXy1KZHvtiVTUmHi1l4RvHRV2/M/te50LBa1dnvtW1XZyN0CoNsdqlffzlH12CeuVwlpTu5rWSvvpqrHvUkPyDuu1nWn7oKyXPUQ4RulXj5RKoFN5cu7ae0j9I1p7Vuw8lVAB+O/hDZXn/MlL+W2adasWbz11lukpaXRtm1b3n//ffr3719r2cmTJ/Pll1/WON6mTRsOHDgAwPz587n99ttrlCkrK8PZ2blO93Qp16cQja3caGboO2tIyS/j0WEteWRYi3pfo8JktllT3RCb43O4/+ud5JYY8HNz5MMJnenT3N+mTGG5kdf+OMSi7cl0auLNMyNj6Bldh2Dv31KUoUaL0VQAq9OrwDSsa+2dvUmbVed1iyvq3hmcnwwrXoZ939se19lBUFs1ih3RB5r2VkF60iY4/Kd6FdTcp7lO9PZq6zSvJqrTu6JQ/Wksq8rCbiiBksyanQqe4aozPKwL+LdS0919ItQ1S3PVc0FuPJTlq/rS6dTLYlF7oFfOBPAIVR36ATGnn85uMkDGfnU9n0gIbF33bPNleaoTIy9BTfmP6F3VCXI2JkPVUrpzIEG1EEL8SzRN41hWCdsTc8kpMZBfamBY6yDrQ8WPO04w7df9dGrqzaZjOdbtVwAm94nkxavaXqA7P0XCOvjz8aoEMLXxa656x1391OhBYSrkH1fT0i3G0593JvYu0GyIWpPW4ko1qp26S02XS9qsGu/qQbh7kCrj7KUa5txjauQ+dbfaG7XFFdD9LnA4Gczt+hp+vV/998i3qqYNnqNLtW1atGgREydOZNasWfTt25dPP/2Uzz//nIMHD9K0adMa5QsKCigrq+pMMZlMdOzYkYceeogXX3wRUEH1I488Qmys7b+d4OC6J/O5VOtTiPPltz2p/N+Pe3h6RAyT+0ZdsPs4kVfK3V/t4GBaoU2HMcCqw5k8s3gf6YW2Hcv/PDaA5oF1DH7+S1J2wO5vVSKzysSiZ9omTNPUevLc+JOvY6pdLck6+coGB1c1pTysi0rclrFfLQmrnj/lbHR2amaZg4vaP7y2JWR6B/V+5drx+rB3Vp0H7kHqGg4ugE7NqMvYf8pSNJ1qy/2aq6n6mkXVg8WkRvuNpapToCTLNokrqKA/vAc0G6yCcyfPkzPw3FT9pe5Wzw9pu9VMg5Fv1v+7nEKCaiGEuEg88PVO/tyXZv25fwt/bu8biaujPTHBHtYkMn8fSGfD0WyGtg6iZ7QvTvZ2lBvNJOaUkJhdyvA2Qda1dOVG87lv+1UbkwF2zFNTzCp7o81GNSWt3XWqR7q2nvuKYtXzfmyVasx8IqseAvxbnnxoONnbnBuvRrLzElQQbKr+MKZTWWNN5TU/oz68msLQ59WIwDc3qjV0fafCFS+d23WruVTbpp49e9KlSxdmz55tPda6dWvGjRvH9OnTz3r+L7/8wrXXXktCQgIRERGACqqnTp1Kfn5+g+/rUq1PIc4XTdP4atNxgr2cubLthc02XWYw8/4/cTw8tAVuJ5ctVWYkB4j0c+XZ0W1YE5dJXmnV/tuapjH2o/V4uzgS5OlMdICbdT13hJ8rDtUyoot6yjykkpOaK1Rg6eShgkwH15OB7ck/PYLVDDT9yWeGiiIVfKbsgPS9kH1EBdrV9xf3DFPtvpu/CnjR1DRyvZ2awaZ3UNuL5SaqTu3KhHKn4+ytngXyEtXIeX24B6kZbsXp6vy6atIL7lhWv8+qhQTVQghxkdA0jZWHM4nNKOKK1kG0CKq997568O3maIeXi4N1T1IXBzsOvTLCWnbK/G0czylhdIdQxnYIOe01L3oWC2Tsg9i/1Pq3tD3quKufmioX0Vc9KOQdV41pXqJaX1aer9ZqW0wq4UtoJ5VIxtUXNs2ColTbz2k/Hq75tFH3GL0U2yaDwYCrqys//PAD11xzjfX4I488wu7du1mzZs1ZrzF27FgqKir4+++/rcfmz5/PnXfeSVhYGGazmU6dOvHKK6/QuXPn016noqKCiooK68+FhYU0adLkkqpPIS5XmqYxauZ6DqcXckffKB4f3sq67tti0awdwIXlRjq8+Het13C01zO+WzivjmsPQF6JgQ3Hsjmaqbb88nKx57ou4XRq4m2zRErTND44meE81NuFMG8Xwn1ciPRzqzWJm6gDi0VlJTcUq63D6pM4zWJRneUZ+1S7XDn93GxQo9FhXVRQXPl3WJKtRrDzj6ufdXpAp0ahK0e5HVxUIO4TaTvSn5ug8sTEr1EZ4ssLq6a9e4ad3BO9k3oeCG7fKAng6trWSy53IYQ4z3Q6HUNbBzG09ZkTg9zQLRwPZ3tWHM4kq6iCEoMZAE9ne7pF+tqUzS0xcCyrhJkrjjBzxRFaBrnTKtgTPzdHWgV7MKFHzWm8dbEnOZ+56xPQ6+DJETFnTErTKPR6Nfod0hEGPa2mvRlK1fYnZ1vLpmmq4XZwsS3b7Q7Y8gmsf081tlED4eqPGzWgvlRlZ2djNpsJCrL9txgUFER6evpZz09LS+Ovv/7im2++sTkeExPD/Pnzad++PYWFhXzwwQf07duXPXv20KJF7WtBp0+fzksvNd7MASHEv6fCZOHazmF0i2xH56a2ibyqB7ZO9noW3tGTjMJyUvLLiM8q5mhWMccySygzmnGpNuMqMaeEB7/ZZXOthZuTiAn24IZuTbitdwT2dnp0Oh17TxSw8rDtiGewpzOj2ocwpmMInU8JxE9ltmjsTs5Dr9PZ3P+qw5l0buptnUF22dDrVbK0hp7r31y96sLNH6IHNuyzKnOzdJvSsPPPIxmpFkKIi4zFonEwrZAKk4Uofzd8XB1qPBwUlhv552AGf+xNY92RLIzmql/lvaJ9+e7u3tafZ60+Spi3C+VGM3mlRvJKDZjNGpH+bnQM96Z9uBdgO5UPwNXRjkeHteT2vpHYX4pT9Epy4Ph6lcjFofE7By7Ftik1NZWwsDA2btxI795V/0Zee+01FixYwOHDh894/vTp03nnnXdITU3F0fH0D50Wi4UuXbowYMAAZs6cWWsZGakW4vJlsWgk5JTgaKe3Zh0vLDcyae5WWgS60yzQnbj0Iv7cl0aFSSXZWv/UYMJ9VNkt8Tn8vjeVlLwyUvLLSMotpdyoyrk52rHj+SusS6QeOJlozWzRMJgtmCwW0vLLySkxMLhVAPNu7wFAVlEF3V/7B70OWgV7EuLlTJCnM8Gezni52NOpqQ+dmnhby/62J5XCMiPFFSZ83Rxp4utKhK8rvm6OeLs64OGsEnAeTi/koW920TXChy4RPnRp6lPvqe/lRjNHM4tpHuh+fpZ+idM6ryPV9ckaCqrhfPnll1m4cCHp6emEh4fz7LPPMmXKxdfLIIQQF5per6NdmNcZy3g6O3Btl3Cu7RJOQamRdUezyCisIKe4gtBqo8s5xRW8tSyW03WfXtM5jPdO7r09tHUg7/8Tx9gOoSTnlbItMY/Xlhxi8a4Unh/Tmj7N/Gucb7Fo5JzMCnvRTbtz82uULN//Jf7+/tjZ2dUYlc7MzKwxen0qTdP44osvmDhx4hkDagC9Xk/37t05cuTIacs4OTnh5ORU95sXQvxn6PU6mgXYJvDydHbglwf62hybNrYtv+5J4dfdqew4nmcNqntG+9lkGK8wmVkbl80fe1Nxd7K3CTy3JuaSVVTBqTyc7fF3d0LTNHQ6HekF5bQK8iA2o4hDaYUcSrNN2PXI0BY2QfUrfxw87febfm1764yx7Yl5HMks5khmMd9tSwbAXq+jqa8rUf5uPDMqxprUbXtiLj/tPEGpwUxJhZlSg8naaaBp8NN9fegaoUbWVx7OYMGm4/Ru5sfwNsFE+tcxo7Y4L+odVC9atIipU6faZA0dOXLkabOGAowfP56MjAzmzp1L8+bNyczMxGQy1VpWCCFE/Xi5OjCmQ2it7xnMFm7tGcHh9EI8nB3wdnXA5+S0toTsEmvjDBAT7MnmZ4bi5+6ExaLxw45kpv91mENphSzbn24NqksqTLzw6wGOZBZxJKOYMqOZFoHuTB3WkpHtgq3BdW6JgXkbElh5OJPiChNlBrN1ut+UflFM7hNp8+CTW2Lg261JpBWU0SzAnZZBHrQIcifA3anGSL3JbCG72EB2cQVN/VzxdK59S67KhyWhODo60rVrV5YvX26zpnr58uVcffWZOyDWrFnD0aNHueOOO876OZqmsXv3btq3b3/O9yyEuHx5uTowqXckk3pHnrGck70dV7QJ4oo2NTsHX7+mPaUGEw52euz1Ohzs9Xg6O9Ah3MtmtLh9uBfLHh1ASn4ZcelFZBSWk15YTkZhOUXlJmKCq3KXBHg4MbZjKJ7O9rg72ZNdbCApt4Sk3FIyiyo4lllsLTumQwhBns5sP57LzuN57EspoNxoIT67hPjsEm7vG2UNquMyivl2a3Kt39Hb1YG8kqos2lsSclkVm8Wq2CxeX3KYlkHuDIkJQkMjv8TIm9d3sJbdeDSbwnIj0QHu5JYYSC8oJ7WgjOJyE37uTkzuE4ldtUSojnb6i6+j/CJX7+nf9c0aunTpUm666Sbi4+Px9fWt8X5dXIpT7IQQ4r8gp7iCmSuOMCgmkMGtAgHYmpDL+E831Vo+JtiDd8d3ok2oJxmF5fR9YyUmS+3NzLhOobx/U2eMZguv/XmIRduSKTOaa5SrPj2v3Gim35uryCmpsI6+2+t1dI/0ZUhMIENbBxJ9cvTjr31pPPDNTsJ9XIkJ9iAm2IPhbYPPOgugri7VtqlyS61PPvmE3r1789lnnzFnzhwOHDhAREQEzzzzDCkpKXz11Vc2502cOJEjR46wefPmGtd86aWX6NWrFy1atKCwsJCZM2eyYMECNmzYQI8ePep0X5dqfQohRHXVE7Wd7v30wnISskuIzypmQMsAIvzUKPPeE/msjs3C1dEONyd7XB3tCHB3okWQB/7ujjadxHEZRaw/ks2q2Ew2Hcup0dYeenmENXncnV9u459DtWfddnGw4+DLV1qvPWX+NtbEZeHj6oiXiz2ujva4ONjh4miHm5Mds27paj338e/3sOlYNgazBaNZo4mvCx3CvekY7kWnJj60qtYR8fGqo7g52tE80IPmge74uzuSVlDO8ZxSTBYLg04+Y1RXZjCTkl9Kcl4Z2UUVFJQZ8XRxYHy3qvXfn609RnG5CUd7PY72ehzs9LQI9KBfi5qz6xrivEz/NhgM7Nixg6efftrm+PDhw9m4cWOt5/z2229069aNGTNmsGDBAtzc3Ljqqqt45ZVXcHE5zwlwhBBCnBM/dydeurqdzTGLpvHYFS1pGeROiyAP/Nwc+XLjcT5fH8/xnFICPNSU3iBPZx4Z2oKmfq6E+7jg7GCHi4MdO5PyeW95HHf2jwbAwU7PwdRCyoxm2oV50q95APFZxcRlFHE8txQn+6rRbGcHO8oMJjQN7PQ6vFwcyC0xsCk+h03xOWw/nsunE7sB0CbUE4sGSbmlJOWW8vfBDEK8XRotqL5U3XjjjeTk5PDyyy+TlpZGu3btWLJkiXV7rLS0NJKSkmzOKSgo4KeffuKDDz6o9Zr5+fncfffdpKen4+XlRefOnVm7dm2dA2ohhPivONsIr16vI9TbhVBvF/o2tw38OoR70yHcu06f0zLIg5ZBHkzpF0VBmZHVsZlsOJqNm5M9Yd4uWKqNm7YI8iCtoJyk3FIC3J0I9nIm2MsZj5Pbo1UP1nNOrj/PLq4gu9h22ryTve068NySCusuJQAFKUb2pxTyzRYI93Fh/VNDrO/9cyiDXUn51p91Oqyd480D3W2C6ls+38zB1ELySo01vne7ME+boPrLjcdJyS+zKXNN57BGC6rrql4j1ZUJTjZs2ECfPn2sx19//XW+/PJLYmNja5wzYsQIVq9ezbBhw3jhhRfIzs7m/vvvZ8iQIXzxxRe1fo4kLxFCiEtPQamR3SfyGdgy4KxlTWaLTfKzHcfzKDOY6dvcz6ZxLzeaqTBa8HKtmt4dl1GEj6sjvm6O2Ol1JGaXsCo2k5WHM7mmcxjXdgkH1BTk4zmlpBaUcTitiNj0Im7vF0lMcOO0IzKy2rikPoUQ4sKrMJnJKzGSU1JBUbmJMqOZMoOZUoMZvQ5rGwtwNLOYUoMaJdah42hmMXtP5LPnRD6J2aVsfHqItZPh+23JLD+UwbGsYo7nlGK2aCcT1bnQMsiD2beqEXBN0+jw0t8Ulaulwh5O9oT5uBDo6YyPqwNR/m5MHdbSeg/v/xNHbokBg8miXmYLPaJ8z7pkoK7Oyz7VDckaOnz4cNatW2ftvQZYvHgx119/PSUlJbWOVr/44ou1brMhDa0QQoiLhQSBjUvqUwghLg8Gk4X8UgP+7k41RvY1TSMxp5Qyg5kwHxe8XGrPmfJvqWvbVK89UhqSNTQkJISwsDBrQA1qDbamaZw4caLWc5555hkKCgqsr+Tk2hfsCyGEEEIIIYS4dDja6wn0dK51qrxOpyPK3402oZ4XPKCuj3oF1dWzhla3fPlym+ng1fXt25fU1FSKi6uy4MXFxaHX6wkPD6/1HCcnJzw9PW1eQgghhBBCCCHExaZeQTXAY489xueff84XX3zBoUOHePTRR0lKSuLee+8F1CjzpEmTrOVvvvlm/Pz8uP322zl48CBr167lySefZMqUKZKoTAghhBBCCCHEJa3e+1TXN2uou7s7y5cv56GHHqJbt274+fkxfvx4Xn311cb7FkIIIYQQQgghxAVQ732qLwRJXiKEEOJiI21T45L6FEIIcbE5L4nKhBBCCCGEEEIIUUWCaiGEEEIIIYQQooEkqBZCCCGEEEIIIRpIgmohhBBCCCGEEKKBJKgWQgghhBBCCCEaSIJqIYQQQgghhBCigSSoFkIIIYQQQgghGkiCaiGEEEIIIYQQooEkqBZCCCGEEEIIIRpIgmohhBBCCCGEEKKBJKgWQgghhBBCCCEayP5C30BdaJoGQGFh4QW+EyGEEEKpbJMq2yhxbqStF0IIcbGpa1t/SQTVRUVFADRp0uQC34kQQghhq6ioCC8vrwt9G5c8aeuFEEJcrM7W1uu0S6CL3WKxkJqaioeHBzqd7pyuVVhYSJMmTUhOTsbT07OR7vC/T+qt/qTOGkbqrWGk3urvXOtM0zSKiooIDQ1Fr5fVVOdK2voLT+qtYaTe6k/qrGGk3hrmXOqtrm39JTFSrdfrCQ8Pb9Rrenp6yj/GBpB6qz+ps4aRemsYqbf6O5c6kxHqxiNt/cVD6q1hpN7qT+qsYaTeGqah9VaXtl661oUQQgghhBBCiAaSoFoIIYQQQgghhGigyy6odnJyYtq0aTg5OV3oW7mkSL3Vn9RZw0i9NYzUW/1Jnf13yd9tw0i9NYzUW/1JnTWM1FvD/Bv1dkkkKhNCCCGEEEIIIS5Gl91ItRBCCCGEEEII0VgkqBZCCCGEEEIIIRpIgmohhBBCCCGEEKKBJKgWQgghhBBCCCEa6LIKqmfNmkVUVBTOzs507dqVdevWXehbuqhMnz6d7t274+HhQWBgIOPGjSM2NtamjKZpvPjii4SGhuLi4sKgQYM4cODABbrji8/06dPR6XRMnTrVekzqrHYpKSnceuut+Pn54erqSqdOndixY4f1fam3mkwmE8899xxRUVG4uLgQHR3Nyy+/jMVisZa53Ott7dq1jB07ltDQUHQ6Hb/88ovN+3Wpn4qKCh566CH8/f1xc3Pjqquu4sSJE//itxDnStr705O2/txJW1930tbXn7T1dXPRtffaZeK7777THBwctDlz5mgHDx7UHnnkEc3NzU07fvz4hb61i8aVV16pzZs3T9u/f7+2e/dubfTo0VrTpk214uJia5k33nhD8/Dw0H766Sdt37592o033qiFhIRohYWFF/DOLw5bt27VIiMjtQ4dOmiPPPKI9bjUWU25ublaRESENnnyZG3Lli1aQkKC9s8//2hHjx61lpF6q+nVV1/V/Pz8tD/++ENLSEjQfvjhB83d3V17//33rWUu93pbsmSJ9uyzz2o//fSTBmg///yzzft1qZ97771XCwsL05YvX67t3LlTGzx4sNaxY0fNZDL9y99GNIS092cmbf25kba+7qStbxhp6+vmYmvvL5ugukePHtq9995rcywmJkZ7+umnL9AdXfwyMzM1QFuzZo2maZpmsVi04OBg7Y033rCWKS8v17y8vLRPPvnkQt3mRaGoqEhr0aKFtnz5cm3gwIHWhlbqrHZPPfWU1q9fv9O+L/VWu9GjR2tTpkyxOXbttddqt956q6ZpUm+nOrWRrUv95Ofnaw4ODtp3331nLZOSkqLp9Xpt6dKl/9q9i4aT9r5+pK2vO2nr60fa+oaRtr7+Lob2/rKY/m0wGNixYwfDhw+3OT58+HA2btx4ge7q4ldQUACAr68vAAkJCaSnp9vUo5OTEwMHDrzs6/GBBx5g9OjRDBs2zOa41FntfvvtN7p168YNN9xAYGAgnTt3Zs6cOdb3pd5q169fP1asWEFcXBwAe/bsYf369YwaNQqQejubutTPjh07MBqNNmVCQ0Np166d1OElQNr7+pO2vu6kra8faesbRtr6c3ch2nv7c7/ti192djZms5mgoCCb40FBQaSnp1+gu7q4aZrGY489Rr9+/WjXrh2Ata5qq8fjx4//6/d4sfjuu+/YuXMn27Ztq/Ge1Fnt4uPjmT17No899hj/+9//2Lp1Kw8//DBOTk5MmjRJ6u00nnrqKQoKCoiJicHOzg6z2cxrr73GhAkTAPn3djZ1qZ/09HQcHR3x8fGpUUbai4uftPf1I2193UlbX3/S1jeMtPXn7kK095dFUF1Jp9PZ/KxpWo1jQnnwwQfZu3cv69evr/Ge1GOV5ORkHnnkEf7++2+cnZ1PW07qzJbFYqFbt268/vrrAHTu3JkDBw4we/ZsJk2aZC0n9WZr0aJFLFy4kG+++Ya2bduye/dupk6dSmhoKLfddpu1nNTbmTWkfqQOLy3y/0DdSFtfN9LWN4y09Q0jbX3j+Tfb+8ti+re/vz92dnY1eh0yMzNr9GAIeOihh/jtt99YtWoV4eHh1uPBwcEAUo/V7Nixg8zMTLp27Yq9vT329vasWbOGmTNnYm9vb60XqTNbISEhtGnTxuZY69atSUpKAuTf2uk8+eSTPP3009x00020b9+eiRMn8uijjzJ9+nRA6u1s6lI/wcHBGAwG8vLyTltGXLykva87aevrTtr6hpG2vmGkrT93F6K9vyyCakdHR7p27cry5cttji9fvpw+ffpcoLu6+GiaxoMPPsjixYtZuXIlUVFRNu9HRUURHBxsU48Gg4E1a9ZctvU4dOhQ9u3bx+7du62vbt26ccstt7B7926io6OlzmrRt2/fGlu4xMXFERERAci/tdMpLS1Fr7f9tW1nZ2fdZkPq7czqUj9du3bFwcHBpkxaWhr79++XOrwESHt/dtLW15+09Q0jbX3DSFt/7i5Ie1/v1GaXqMotNubOnasdPHhQmzp1qubm5qYlJiZe6Fu7aNx3332al5eXtnr1ai0tLc36Ki0ttZZ54403NC8vL23x4sXavn37tAkTJlx2KfzPpnpGUE2TOqvN1q1bNXt7e+21117Tjhw5on399deaq6urtnDhQmsZqbeabrvtNi0sLMy6zcbixYs1f39/7f/+7/+sZS73eisqKtJ27dql7dq1SwO0d999V9u1a5d1O6W61M+9996rhYeHa//884+2c+dObciQIbKl1iVE2vszk7a+cUhbf3bS1jeMtPV1c7G195dNUK1pmvbxxx9rERERmqOjo9alSxfr9hFCAWp9zZs3z1rGYrFo06ZN04KDgzUnJydtwIAB2r59+y7cTV+ETm1opc5q9/vvv2vt2rXTnJyctJiYGO2zzz6zeV/qrabCwkLtkUce0Zo2bao5Oztr0dHR2rPPPqtVVFRYy1zu9bZq1apaf4/ddtttmqbVrX7Kysq0Bx98UPP19dVcXFy0MWPGaElJSRfg24iGkvb+9KStbxzS1teNtPX1J2193Vxs7b1O0zSt/uPbQgghhBBCCCGEuCzWVAshhBBCCCGEEOeDBNVCCCGEEEIIIUQDSVAthBBCCCGEEEI0kATVQgghhBBCCCFEA0lQLYQQQgghhBBCNJAE1UIIIYQQQgghRANJUC2EEEIIIYQQQjSQBNVCCCGEEEIIIUQDSVAthBBCCCGEEEI0kATVQgghhBBCCCFEA0lQLYQQQgghhBBCNJAE1UIIIYQQQgghRANJUC2EEEIIIYQQQjSQBNVCCCGEEEIIIUQDSVAthBBCCCGEEEI0kATVQgghhBBCCCFEA0lQLYQQQgghhBBCNJAE1UIIIYQQQgghRANJUC2EEEIIIYQQQjSQBNVCCCGEEEIIIUQDSVAthBBCCCGEEEI0kATVQgghhBBCCCFEA0lQLYQQQgghhBBCNJAE1UIIIYQQQgghRANJUC2EEEIIIYQQQjSQBNVCCCGEEEIIIUQDSVAthBBCCCGEEEI0kATVQgghhBBCCCFEA0lQLYQQQgghhBBCNJAE1UIIIYQQQgghRANJUC2EEEIIIYQQQjSQBNVCCCGEEEIIIUQDSVAthBBCCCGEEEI0kATVQgghhBBCCCFEA0lQLYQQQgghhBBCNJAE1UIIIYQQQgghRANJUC2EEEIIIYQQQjSQBNVCCCGEEEIIIUQDSVAthBBCCCGEEEI0kATVQgghhBBCCCFEA0lQLYQQQgghhBBCNJAE1UIIIYQQQgghRANJUC0uexs3buTFF18kPz+/0a89efJkIiMjG/26QgghhLg4rV69Gp1Ox+rVqy/0rQgh/iUSVIvL3saNG3nppZfOS1D9/PPP8/PPPzf6dYUQQgghhBAXB/sLfQNCXErKyspwcXGpc/lmzZqdx7u5tJjNZkwmE05OThf6VoQQQghxDoxGIzqdDnt7CSWEABmpFpe5F198kSeffBKAqKgodDqddcpWZGQkY8aMYfHixXTu3BlnZ2deeuklAD7++GMGDBhAYGAgbm5utG/fnhkzZmA0Gm2uX9v0b51Ox4MPPsiCBQto3bo1rq6udOzYkT/++KPe9//SSy/Rs2dPfH198fT0pEuXLsydOxdN02qU/eabb+jduzfu7u64u7vTqVMn5s6da1Nm6dKlDB06FC8vL1xdXWndujXTp0+3vj9o0CAGDRpU49qnfs/ExER0Oh0zZszg1VdfJSoqCicnJ1atWkV5eTmPP/44nTp1wsvLC19fX3r37s2vv/5a47oWi4UPP/yQTp064eLigre3N7169eK3334D4I477sDX15fS0tIa5w4ZMoS2bdvWtSqFEEJcpn755Rd0Oh0rVqyo8d7s2bPR6XTs3buX7du3c9NNNxEZGYmLiwuRkZFMmDCB48ePn/M9NGbbWOls7X5kZCSTJ0+ucf1T2/rK6ewLFizg8ccfJywsDCcnJ44ePUpWVhb3338/bdq0wd3dncDAQIYMGcK6detqXLeiooKXX36Z1q1b4+zsjJ+fH4MHD2bjxo0ADB06lJiYmBrPMJqm0bx5c0aPHl2fKhXiXyXdS+Kyduedd5Kbm8uHH37I4sWLCQkJAaBNmzYA7Ny5k0OHDvHcc88RFRWFm5sbAMeOHePmm28mKioKR0dH9uzZw2uvvcbhw4f54osvzvq5f/75J9u2bePll1/G3d2dGTNmcM011xAbG0t0dHSd7z8xMZF77rmHpk2bArB582YeeughUlJSeOGFF6zlXnjhBV555RWuvfZaHn/8cby8vNi/f7/Ng8DcuXO56667GDhwIJ988gmBgYHExcWxf//+Ot/PqWbOnEnLli15++238fT0pEWLFlRUVJCbm8sTTzxBWFgYBoOBf/75h2uvvZZ58+YxadIk6/mTJ09m4cKF3HHHHbz88ss4Ojqyc+dOEhMTAXjkkUf44osv+Oabb7jzzjut5x08eJBVq1bx8ccfN/jehRBCXB7GjBlDYGAg8+bNY+jQoTbvzZ8/ny5dutChQwd+/PFHWrVqxU033YSvry9paWnMnj2b7t27c/DgQfz9/Rt8D43ZNkLd2v36euaZZ+jduzeffPIJer2ewMBAsrKyAJg2bRrBwcEUFxfz888/M2jQIFasWGENzk0mEyNHjmTdunVMnTqVIUOGYDKZ2Lx5M0lJSfTp04dHHnmEq6++mhUrVjBs2DDr5/71118cO3aMmTNnNvjehTjvNCEuc2+99ZYGaAkJCTbHIyIiNDs7Oy02NvaM55vNZs1oNGpfffWVZmdnp+Xm5lrfu+2227SIiAib8oAWFBSkFRYWWo+lp6drer1emz59eoO/R+V9vPzyy5qfn59msVg0TdO0+Ph4zc7OTrvllltOe25RUZHm6emp9evXz3pebQYOHKgNHDiwxvFTv2dCQoIGaM2aNdMMBsMZ79tkMmlGo1G74447tM6dO1uPr127VgO0Z5999oznDxw4UOvUqZPNsfvuu0/z9PTUioqKzniuEEIIoWma9thjj2kuLi5afn6+9djBgwc1QPvwww9rPcdkMmnFxcWam5ub9sEHH1iPr1q1SgO0VatWNfh+zqVtrEu7r2nqOee2226rcfzUtr7y+wwYMKDO9z106FDtmmuusR7/6quvNECbM2fOac81m81adHS0dvXVV9scHzlypNasWbMzPp8IcaHJ9G8hzqBDhw60bNmyxvFdu3Zx1VVX4efnh52dHQ4ODkyaNAmz2UxcXNxZrzt48GA8PDysPwcFBREYGFjvHuSVK1cybNgwvLy8rPfxwgsvkJOTQ2ZmJgDLly/HbDbzwAMPnPY6GzdupLCwkPvvvx+dTleveziTq666CgcHhxrHf/jhB/r27Yu7uzv29vY4ODgwd+5cDh06ZC3z119/AZzxvkGNVu/evZsNGzYAUFhYyIIFC7jttttwd3dvtO8ihBDiv2vKlCmUlZWxaNEi67F58+bh5OTEzTffDEBxcTFPPfUUzZs3x97eHnt7e9zd3SkpKbFpvxqqsdrGurT7DXHdddfVevyTTz6hS5cuODs7W+97xYoVNe7b2dmZKVOmnPb6er2eBx98kD/++IOkpCRAzQxcunRpoz+fCNHYJKgW4gwqp4NXl5SURP/+/UlJSeGDDz5g3bp1bNu2zTrVuKys7KzX9fPzq3HMycmpTudW2rp1K8OHDwdgzpw5bNiwgW3btvHss8/a3Efl1Kzw8PDTXqsuZRqitvpbvHgx48ePJywsjIULF7Jp0ya2bdvGlClTKC8vt7knOzs7goODz/gZV199NZGRkdb6nz9/PiUlJY3+MCGEEOK/q23btnTv3p158+YBKrnmwoULufrqq/H19QXg5ptv5qOPPuLOO+9k2bJlbN26lW3bthEQEFCv9rs2jdk2/ptt+rvvvst9991Hz549+emnn9i8eTPbtm1jxIgRNnWSlZVFaGgoev2ZQ48pU6bg4uLCJ598AqgcNi4uLmcMxoW4GMiaaiHOoLZe0V9++YWSkhIWL15MRESE9fju3bv/xTuD7777DgcHB/744w+cnZ1t7q+6gIAAAE6cOEGTJk1qvVb1Mmfi7OxMQUFBjePZ2dm1lq+t/hYuXEhUVBSLFi2yeb+ioqLGPZnNZtLT02ttyCvp9XoeeOAB/ve///HOO+8wa9Yshg4dSqtWrc74XYQQQojqbr/9du6//34OHTpEfHw8aWlp3H777QAUFBTwxx9/MG3aNJ5++mnrOZVroc9VY7aNdWn3QbXpp14fVJte2/rw07XpgwYNYvbs2TbHi4qKatzT+vXrsVgsZwysvby8uO222/j888954oknmDdvHjfffDPe3t6nPUeIi4GMVIvLXuUWT3XtZa5sVKpvDaVpGnPmzGn8mzvLfdjb22NnZ2c9VlZWxoIFC2zKDR8+HDs7uxoNXnV9+vTBy8uLTz75pNbM4ZUiIyOJi4uzaYRzcnKsmTvret+Ojo42jXN6enqNDKcjR44EOON9V7rzzjtxdHTklltuITY2lgcffLDO9yOEEEIATJgwAWdnZ+bPn8/8+fMJCwuzzgjT6XRomlZjW8jPP/8cs9l8zp/dmG1jXdp9UG363r17bY7FxcURGxtbr/s+tU727t3Lpk2batx3eXk58+fPP+s1H374YbKzs7n++uvJz8+XNl1cEmSkWlz22rdvD8AHH3zAbbfdhoODwxlHOa+44gocHR2ZMGEC//d//0d5eTmzZ88mLy/v37plAEaPHs27777LzTffzN13301OTg5vv/12jcYtMjKS//3vf7zyyiuUlZUxYcIEvLy8OHjwINnZ2bz00ku4u7vzzjvvcOeddzJs2DDuuusugoKCOHr0KHv27OGjjz4CYOLEiXz66afceuut3HXXXeTk5DBjxgw8PT3rfN+V25Tdf//9XH/99SQnJ/PKK68QEhLCkSNHrOX69+/PxIkTefXVV8nIyGDMmDE4OTmxa9cuXF1deeihh6xlvb29mTRpErNnzyYiIoKxY8eeY+0KIYS43Hh7e3PNNdcwf/588vPzeeKJJ6yjqp6engwYMIC33noLf39/IiMjWbNmDXPnzm2UUdTGbBvr0u6DatNvvfVW7r//fq677jqOHz/OjBkzrCPddb3vV155hWnTpjFw4EBiY2N5+eWXiYqKwmQyWctNmDCBefPmce+99xIbG8vgwYOxWCxs2bKF1q1bc9NNN1nLtmzZkhEjRvDXX3/Rr18/OnbseM71K8R5d4ETpQlxUXjmmWe00NBQTa/XWzN2RkREaKNHj661/O+//6517NhRc3Z21sLCwrQnn3xS++uvv2pk+zxd9u8HHnigxjVPl4XzTL744gutVatWmpOTkxYdHa1Nnz5dmzt3bq3ZzL/66iute/fumrOzs+bu7q517txZmzdvnk2ZJUuWaAMHDtTc3Nw0V1dXrU2bNtqbb75pU+bLL7/UWrdurTk7O2tt2rTRFi1adNrs32+99Vat9/3GG29okZGRmpOTk9a6dWttzpw52rRp07RTfyWZzWbtvffe09q1a6c5OjpqXl5eWu/evbXff/+9xjVXr16tAdobb7xR9woUQgghqvn77781QAO0uLg4m/dOnDihXXfddZqPj4/m4eGhjRgxQtu/f3+N9ruh2b8bu208W7tvsVi0GTNmaNHR0Zqzs7PWrVs3beXKlafN/v3DDz/UuOeKigrtiSee0MLCwjRnZ2etS5cu2i+//FLr809ZWZn2wgsvaC1atNAcHR01Pz8/bciQIdrGjRtrXHf+/PkaoH333Xf1qkMhLhSdpp1hrqcQQlwiHn/8cWbPnk1ycnKtieCEEEIIcWm47rrr2Lx5M4mJibXuIiLExUamfwshLmmbN28mLi6OWbNmcc8990hALYQQQlyCKioq2LlzJ1u3buXnn3/m3XfflYBaXDJkpFqIi1D1dUi10ev1Z92W4nKh0+lwdXVl1KhRzJs3T/amFkIIcdHQNO2siczs7OxkD2YgMTGRqKgoPD09rduXVU/GKsTFTIJqIS4ylY3KmUybNo0XX3zx37khIYQQQjTI6tWrGTx48BnLzJs3j8mTJ/87NySEOC8kqBbiImMwGGpscXGq0NBQQkND/6U7EkIIIURDFBUVnXWLqqioKFm6JMQlToJqIYQQQgghhBCigeq9KHPt2rWMHTuW0NBQdDodv/zyy1nPWbNmDV27dsXZ2Zno6Gg++eSThtyrEEIIIYQQQghxUal39u+SkhI6duzI7bffznXXXXfW8gkJCYwaNYq77rqLhQsXsmHDBu6//34CAgLqdD6AxWIhNTUVDw8PSeQghBDioqBpGkVFRYSGhkriwEYgbb0QQoiLTV3b+nOa/q3T6fj5558ZN27cacs89dRT/Pbbbxw6dMh67N5772XPnj1s2rSpTp9z4sQJmjRp0tDbFEIIIc6b5ORkwsPDL/RtXPKkrRdCCHGxOltbf973qd60aRPDhw+3OXbllVcyd+5cjEZjrfvPVVRUUFFRYf25Mu5PTk7G09Pz/N6wEEIIUQeFhYU0adIEDw+PC30r/wmV9ShtvRBCiItFXdv68x5Up6enExQUZHMsKCgIk8lEdnY2ISEhNc6ZPn06L730Uo3jnp6e0tAKIYS4qMhU5cZRWY/S1gshhLjYnK2t/1cWgZ16E5Ujz6e7uWeeeYaCggLrKzk5+bzfoxBCCCGEEEIIUV/nfaQ6ODiY9PR0m2OZmZnY29ufdk8+JycnnJyczvetCSGEEEIIIYQQ5+S8j1T37t2b5cuX2xz7+++/6datW63rqYUQQgghhBBCiEtFvYPq4uJidu/eze7duwG1Zdbu3btJSkoC1NTtSZMmWcvfe++9HD9+nMcee4xDhw7xxRdfMHfuXJ544onG+QZCCCGEEEIIIcQFUu/p39u3b2fw4MHWnx977DEAbrvtNubPn09aWpo1wAaIiopiyZIlPProo3z88ceEhoYyc+bMOu9RLYQQQgghhBBCXKzOaZ/qf0thYSFeXl4UFBRIRlAhhBAXBWmbGpfUpxBCiItNXdumfyX7txBCCCGEEEII8V8kQbUQQgghhBBCCNFAElQLIYQQQgghhBANJEG1EEIIIYQQQgjRQBJUCyGEEEIIIYQQDSRBtRBCCCGEEEII0UASVAshhBBCCCGEEA0kQbUQQgghhBBCCNFAElQLIYQQQgghhBANZH+hb0AIIYQQQgghxKWv1GDi7wMZ7ErKY2jrIAa0DKjX+UazheM5JTQP9LAe+3V3CgdTCyk1mCkzmik3mmkV5MGEnk3xd3eyOb+w3EiF0UKAh9Oplz6vJKgWQgjRKAwmC/M3JrBoWzL9WwRw14BowrxdznhOQZkRg6lujV9BqZGk3FJ83R0J9XJGp9Od9Zxyo5nN8TkMahVY5+8hhBBCiLMrN5rJLTHg6eKAu5MKK1/87QDfbz8BwJebjjOuUyjPj2mD3ynB77ID6fxzMIMwHxdCvV0wmi2sjcti49EcAHa+cAUOdmpS9d8HMvhzX5rN+X+Qxocrj3J1p1CmXdUWdyd71sZlcedX27m5R1NevKrt+f76NiSoFkIIUWeapnEirwwnBz0B7k7WwPZweiEPfL2TY1klABzLKmHh5uNc3SmMyX0iaR/uZb3GuiNZrDiUydaEXA6lF6JpMKBlAJP7RDCoZSB6vbrmgdQCFm5O4lBaIcdzSsgrNVqvEezpzDOjYri6UxgAq2Iz+XZLEhlFFZgtFmu5tPxycksNLH90gE2vtxBCCPFfZTJbOJZVQkJ2MUGezjQPdMfD2cGmzJ7kfJbsS+NEfhkpeWWk5JdRWmHCx80RXzdHpg5rwZCYIADWxGUxY+lhNUp8crS41GCmwqTa2w8ndGZsx1AAxnQIZXN8Lh3CvViyL41fdqeyJi6LsR1DeXpkDK6OKvzclpDLDztO1Hr/Pq4OJOeWEh3gDsDQ1oGEejvj4miPi4Md9nodf+5LY3dyPruS83FztAOgQ7gXmqZxOL2w8Sv1LCSoFkKI/5jCciM7EvMAcLLX43jy1SHc21pmx/FcsooMuDra4epoR7iPK8FezrVer6TCxMZjOayOzWR1bBYp+WUAeDjbs+7/BuPt6kighzPZxQb83By5a0C06m0+lsNPO0+w7EA6+1+60nq9z9bGs+5Its1nrI3LYm1cFl2aevPTfX3Q6XRkFJbz7dYkm3L+7o7klRpJLyzHw7mqCcsoKOfvgxk17t2DUkK8fEkvqJCgWgghxEUls6icnGIDMcEedZp9dTZ7T+Tz4m8HOJhWSLnRYvNekKcTH9/chW6RvgAk5pTw6dr4GtcoMZRxIq/M5vwyg4kDqbUHqvZ6HaUGk/Xnfs39WfPkIHQ6HXuS83nqp70cTi/iq03HaRfmxfhuTQAY1iYILxcHUvJVQG8ya/Rp5seAlgG0C/PCTl9VH9d2Ca/xuXcNiGZnUh6lFWZr3Xm7OrLy8UGE+5x5ltz5IEG1EEL8R2w4ms2ibcksO5Bu7T2uZK/XcfT1Udaf56xNYOmBdOvPeh2M6xTGw0NbEOnvZj3+6ZpjzFxxhBKD2XrMwU6H2aKhA7xcVM+3r5sjcyZ1o1WwB14uDtw7sBm7k/OZvfooa+Ky0DTN2ui1DfUi2t+NHlF+dI/yodxgYcHmRBZtS6Zvc39ruT7N/JncJ5IeUb5E+rkR4eeKm5M9pQYTe08U0DbU03pPPaJ8efnqtgR5OuNor8e+Io+Yrc8ScGI5pp5PY99iaONVtBBCiMvOrqQ8XvvzEIk5pbQMcufegc3o38K/zsFwSn4Zby+LZVT7EK5oo0aAtyXk8cA3O+kV7cuTV7aia4QKePenFBDk6WxdGpWQXcLPO09wY4+mZ1xW5epox86kfADcHO2IDnAnvbCcrKIKMgor2JmUZw2qu0b4MLlPJOE+LoT7uBDm7Yq7sz25JQbySgx0qDbDrEuED/Nu746rgx0ujna4ONjh7GCHl6sDHk72NnWgrxYMd2zize8P9WPOunh+2ZVChbHqWaJXtB+9ov3qVHen06WpT41jTXxdz+maDaXTNE27IJ9cD4WFhXh5eVFQUICnp+fZTxBCiMtA5a/vysZs6ne7+GV3KgARfq54ONtjMFmoMFkwmTVWPznIuj7prWWH2RyfS6nBTKnBxPGcUgDs9DrGdwvntXHt0et1fL4unlf/PEQTXxcGtwpkUKsAekX7oT85khzh51bLnTWAsRzDjgXYbfkYu4oi6DAeutwGgTGVXxZyjkHyFrB3gsDW4NcC7B1rXithHSy+G4pSq45dPQs639I493qStE2NS+pTiMtbfqmBgjJjo7QrZQYz0/86xNiOoXRt6mMN9DRNI62gnNCz5PvQNI3VcVkEejjRNlQFl/tTChjz4Xqbch3CvbhvYDNCvF3o1MTbenxrQi5F5Ua6Rfii18Ps1ceYuz6BCpOFV8e149ZeEQBsT8xl/KebsJyMxga1CsDX1ZGfd6dwU/emTL+2PQCvLznEZ2vj0ekgJtiTMG8XwrydcXNS7fxzY9oAYLZo/L4nlXZhqvO68nsXlBo5mlWMk72edmFVwbI4u7q2TRJUCyHERSSvxIBOp6YwAcSmF3HHl9uI9HPDx80RRzs1lRs01sZl89mkrtYGf3N8Dn/tS+O6ruG0D/Oq11SyvSfyeW95HKtiM/m/6CTub10GhlKM5cVk5uQR2rwDuk43g6vvmS9UUQQJa8EnSgW+1e/BYobUXZAVC45u4OwJTp6QvBU2zoSitJrXa9ob3APh+EYoybJ9T28Pvs3ANwo8w8ArDEpyYPMsQFNBd9NesGuBKnvrYogeWOc6ORtpmxqX1KcQl4+MwnK+3nycO/pH4+XigNmicfv8bexOymPmhM42ySUtFs1m9LMuftuTysPf7gIgzNuFQa0CSMotZX9KAXmlRrY+O5RAD7XkyWCy4GCnI7vYwNHMYo5kFvHd1mQOphUyJCaQLyZ3B1RSrmUH0gn3ceGPvWl8uzXJOkXaXq/j0CsjrB3Xd321neUHM9DpwMXBjtKTs716Rvny0tVtiQmu+h2Xkl/GhyuO8MOOE5gtVWHZtV3CePv6juj1Ov45mMEXGxLYeCynxnfV6WDJw/1pHSK/N8+HurZNMv1bCCEuAuVGM19uTOSjVUcZ1ymMV8a1AyAhu5gTeWp9U22W7EuzBtXnMpWqQ5gX8/rkUlr0Oq6pB+DkIK8DEAYQvwhWvAztroXud0JYV9uAWdNg34/w93NQfHJauXswRA+CoLZwYqsKtssLTn8TnmHQ9xHwiYSdX0HsX5C0qep9Oyf1uZoZMg9BRSFkx6rXqTpPhJFvgr0LGErgwGL4fiLcsRwCWjWoji43s2bN4q233iItLY22bdvy/vvv079//9OW//jjj/noo49ITEykadOmPPvss0yaNOlfvGMhxMXOYtH4estx3lwaS3GFiQAPJyb2jqS43ERRuZHCchO3z9/GE8NbMbhVIO8ujyPK35VnR6uR2IJSI7fO3cI1ncPo3NTbOhU5ObeMfw5lMG1sG3Q6HdH+blzfNZyl+9NJyS/j6y1V+Tns9Tris0qsQfVrfx7k223JGE5ZNuXqaEezADdrUO/sYGdNjtk1wpcHBzdn3oZEFm1PxtlBT3ZxBSFeagQ8yt+NaH834rNLKDWYifZ34+mRMVzRJqhGh3eYtwtvXNeBewc246NVR8ktMfDw0BY2I9/D2gQxrE0QJ/JKOZJRbF2HnFFYTrcIX1oEujf635WoHxmpFkKI80jTNBKyS4hNL2Jk+xDr8SMZRRxOL2LviXx2J+ezL6XA2uPdPsyLxff3wcFOT1G5kcPpRSRml1BUbsJgtlBhtGCyWGgb6smgVoE4O9g1/AYNJXD0H9gwE1K2q2OO7hAzGpy9wMEV7Bwhbimk7606z7spRPRVI8k+kbBmBhw/OS3OPVgFz6ZaOgKcvSCkE5gNalS7vFCNWPe4GzpOsJ3OXZgG+34AixGa9oGwLmrqt6pYKEyBzMNQkAQFKern8gLocCO0HVd1HWM5fHWVmjruHQF3rgD3+u2bWZv/ctu0aNEiJk6cyKxZs+jbty+ffvopn3/+OQcPHqRp06Y1ys+ePZunnnqKOXPm0L17d7Zu3cpdd93FN998w9ixY+v0mf/l+hRCqHbv6cX72HFcJdLs2MSbJ4a3pH8L9fu4wmTmxd8O1khQ6epox5b/DcXD2YH5GxJ48feDp/2M92/sxLjOYdafy41m/jmUwa6kfKL83Wgf5kWrYA9ru2mxaPR5YyXpheXodNDEx5Xmge50aerNLT0j8HGrZYlRPWQWlXMir4z2YV7WUWxxaZHp30II8S9bHZtJUm4pReUmCsuNpBeUsyU+l/TCcvQ62D1tOJ4nt7R48oc9NbaSCPFy5vHhrbimc5hN1svTKs6CjH2Qvh9y4yG0E7S+ynaKduZhNf35xHbwDAW/ZuAbDRaTGgk+thJM5aqsgyv0uAv6PAJup4x4axqk7IBtn8P+xWCuqHk/9i4w4HHo/ZD6OXkLxK+CrDgI7QzNBqs/9efQCdBQJdnw+VDIS4Tud8Hot8/5kv/ltqlnz5506dKF2bNnW4+1bt2acePGMX369Brl+/TpQ9++fXnrrbesx6ZOncr27dtZv359jfK1+S/XpxD/JQVlRnQ6rO2ZwWQ5uSypdgnZJczfkMA3W5MwmjXcHO34vxEx3Norota27pstSUz7bT9Gs8boDiE8OqwlzU+OxBaWG/l1dyo/7zxBVnEFZQYL5UYzJouFqzuG8fzYNtb9kuv8fUqNZBSV09TX9dw6qcV/kkz/FkKIRpSQXcK6I1kYTBY8nO3xcHbATq9jaEwg9id7n/8+mME3W5JqnOtop6dzU29yig3WhxAvFwc6NvGmfZgnnZr40Js9hDjmo28bolJx18ZQAvGr1ajx0RVqZLa6HcCfj0P0YIjorYLmE9vO/uW8I6DtNdD7AbV+uTY6HYR3U69Rb6vp3Mc3qlfGAbVWefiragS7UvTARl3DfE7c/OGWH9Xa7eGvXOi7uagZDAZ27NjB008/bXN8+PDhbNy4sdZzKioqcHa23ZLNxcWFrVu3YjQacXBwqPWcioqqzpnCwn9/X1EhLnaJ2SW4OtlZpypfaBaLxmOLdhOfXcKnE7tSWGbkke9289HNnelcSyZmgFmrjlo7kYfGBPLKuHZnTBR2c8+m9G3uh9miWfcpruTp7MDEXhFMPJnoqzF4uTrg5Vrzd5QQ9SFBtRBCnMHS/enMWHqY+OySWt//8d7e1u0pruoYSl6JwRp0e7s40CXCh64RPjV6vyszdVKcBX89CQd+Vj9veA9GzoCIPupnsxHilqnR5mOrThkh1qlR5+B2KpiNXw3p++DocvUClaCr5QhoNQrKctWIds4xMFVA86FqmndgG9v10Wfj5A7NhqjXpcS/BVz14YW+i4tednY2ZrOZoKAgm+NBQUGkp6fXes6VV17J559/zrhx4+jSpQs7duzgiy++wGg0kp2dTUhISI1zpk+fzksvvXRevoMQ/wWJ2SVc+f5aAj2dWPX4IGsH7oX00aqjrDiciaO9HoPJwryNiaTkl3Hvwh38/mA/Aj2d2Xg0mya+rtatjW7vG0V2cQV39Iumb3O/OiXRbLSdJUTdaZpKKGrXgPAwPxn2LlKzwowlYCxTM+KaD4N214NDI3YKHVkOS55UAwsOLmqWnYOrmq0XMwZaXAFOHo33eXUkQbUQQlSTU6yCVj93tXbX2UFPfHYJ9nodPaJ8CfBwOplQxUSJwURmUVWQW69EYZWJvf76PxXs6uxURuz0fTBvJLS9VmW13rUQijOqzvNuCi1HQsvh0KSXCnCryz6qknKl7FBrnjvedPrRZyHO4NQH3+p7jZ/q+eefJz09nV69eqFpGkFBQUyePJkZM2ZgZ1f7dMpnnnmGxx57zPpzYWEhTZo0abwvIMQlbv7GRCpMFpJzy1h7JIshMUFnP+k8WhWbyXv/xAHw6rh2tAvz4s3rOhCXXsSRzGLu+mo7bk72bDyWwzWdw3jvxk4AtAn1ZN7tPS7gnV/ELGaVX8TFu+Z7FcWwerrKJTL4OdCfQ6dKaa7qoM/Yr5KHVuZD0enUbLO9i9QzSXGmCk6b9lad+4Ft1LOJg4ta4lX9HjRN7d6xeRYc+l0lET3VgZ/hnxeh2x3QdbIKtAtOQEEylOWDZwh4hYNXU3XttD2Qulv9qdNBm6uhxZUqKC8vhGX/U4MMlcyGqgSoucdg/08qqWmzISqxaofxDa+zepKgWghx2SsoNbIqNpOl+9NZcTiDKf2ieGZka0AFyh/f3IX+Lf2tU7fRNLVG+cBvkGSANBdwcFM9oyEdIKwbOLqe/gNLsuG3hyH2T/VzUDu4+iPwagIrX4Ud81VgXMktADrdogLkgJgzjyr7N4eB/3duFSIua/7+/tjZ2dUYlc7MzKwxel3JxcWFL774gk8//ZSMjAxCQkL47LPP8PDwwN/fv9ZznJyccHJyavT7F+K/oLDcyA/bk60//7jjRL2D6lKDiQ1Hc2gf5kWw17mNFB7NLGbqd7vRNDU9e3w31QHm7mTPnEnduOqj9ew5oYIbRzs93q4OZ+yIu6BMFVCao/KMnMpihm9vUu30xMXgUvuU9tNf26CSf1ZUW85i5whRA9QypEqaBod+g+XTID8Jet0HA59SiTtBBbrf3wY5R9TPOjsY8qztZ6XsgB/vUAGqlU5tL+nfCgJaqueHYyshYV3NoNcjVH1e1mHb4ye2qdfGmTW/n71L1eiwTmf72VEDqp5/HFxVp8DOr6DwBKx5Q73qa/9P4OQFrcdCwpqTn6eDXvdDz3tUUG0ogfJ89T0P/a5m5MX9pb7vvxhUS6IyIcRlR9M0EnNKWR2byYpDmWyOz8FUbW/Iwa0Cau9VryhW2ai3z1Ujyqejd1CZqiP6qqlIYV2qAuEj/8Cv96vRZ72DCoD7TrXNep22F9a8qXp0O05QU7ftzy0D6eWgxFjC34l/0zWoK009a2aobmz/5bapZ8+edO3alVmzZlmPtWnThquvvrrWRGW1GThwIGFhYXzzzTd1Kv9frk8h6mvu+gRe+eMgAR5OjGwXzA1dm9A+3KtGuf0pBTQPdK+xxEjT1L7Pq2OzAGgV5MGAlv4MbBlIvxa1d3SdTkp+GdfP3khaQTmdmniz6J5eONnbft6Go9k8+/M+ekb58fCwFoSdYc10ozOWweE/VZvrWXOpiZXJADu/hLVvQ0kmjP9KBWvVbZsLf56cQRMzBm5cWPflUUXp8P0klaTzVHp7aDZUBXmeofDPS5C82baMexBc8Ypq+/98XO2g4eKrZrMBjF8Aba5S/522F74cc+ZtKk8V2Baa9lTJTVN3qZ01QD2LtLxS3VtQOzX6nLQRjm9SQWxlMtPa2DlBhxtUkBvUtub7ZqMKdDfPUoG63kEF/V5N1G4gRenqMypn5HlHqJHykE4qUN73o23+GO8IGDcbIvvWfj+aprbcPPS7WhoXM7ru9XMakv1bCHHZKakwsSspn1bBHgR4nH4ETNM0+r25ipT8qi2fWgV5MLxtECPaBVv3fa52Auz5FpY+o37JA9g7q+Re3k1VL6mxVPV8J2+FojTb8ysTgRlLYetn6lhADFz3OQS3b4RvfulYnbya1cmrGd9qPG382tTr3LzyPN7b8R4dAzpyXcvrbN6LzY3l8TWPc7zwOHY6O65veT3P9nz2vI6S/JfbpsottT755BN69+7NZ599xpw5czhw4AARERE888wzpKSk8NVXXwEQFxfH1q1b6dmzJ3l5ebz77rssX76cHTt2EBkZWafP/C/XpxBnsiYui193pfD2DR3Rn0xUedVH69l7ooBXx7Xj1tMk5fpuaxJPL95H1wgfvr6zp01g/e3WJJ5ZvA87vQ6LplH5tN8qyINljw6wlnvg651kFJbj4miHs4Md0f5uTOoTaRMU3/3Vdv4+mEGzADd+uLcPvue4zVSDFGeqEd9Tp0mf2AG/3AvZcWo7x4mLawZ3ZhPs/Q5Wv6m2YKzkFgD3b6na7aIsD2Z2qQpiQSXm7HHX2e8veSssmgjF6WpkNbyb7b1n1NIRb+8CfR5SQeTfz6kR1uqaDYVrP4N178Lmj9WMuDuXg04P80erZ47wHupZwu7k34nFpHa5yI5VO28UpkB4d9V54Nes6tqGUrWNZkmWSm5afdeQU1ksKsA3lKrnGGOZ+tNUrkbET90t5HTKC8DRo/Zp7KYK9XI+5Xe/xQLHN5wcsfZQo/mnLns7zyT7txDisvPTzhO88OsB9Dro3cyPMR1CaRfqxdIDaWw6lsMP9/bBTq9Dp9MxrHUgRzKLGdQqgCvaBBPl76YavtJUsLhXbftUmAq/T4Ujy9TPvtFqbVCnm2tvhDRNNWjHN6opYHFLIf84bHi/qkyPu+GKl9UUqotcekk6T619Ci8nL57s9iRNPM9tzaurvSs/HfmJn478xKioUTzY+UGaeKhrWjQLel3ta8biC+J5cMWDJBclMyJyhPW4pmn8dOQn3tj6BhXmCtwc3CgxlmCymC7OaYeXiBtvvJGcnBxefvll0tLSaNeuHUuWLCEiQj3cp6WlkZRU9XBqNpt55513iI2NxcHBgcGDB7Nx48Y6B9RCXG4OpRXi6miHh7MD9y7YQZnRTKem3kzqHQnAd3f34pddqYzrXMsU5ZMqc3rsOJ7H/xbv453xHdHpdBSWG3n9z0MAPD0ihuu7hrP+aDZr47JwcrD9Hbs2LouiCpPNsbnrE7i2Sxj3DmxGdIA7b17XAXu7fTw/ps2/H1CbTar9XH1y6nDLK6H9DWrN7Ib3Yf17oFnUe8XpKifJhEVqBwxQo61LnlBriUGNBvd/HLbPg6xDsPQpFZQCrJquAuqA1qqNX/48LHsWmvRUS7sAjOVqanFZ/skEWS6qjf/nJTXyGxADN31jG8CCCnD3fa/WLucnq+sPflaN2oJK6LXxQzWKbq6Awf+Dfo+rAPSKlyHzgEpG+u1NVdPXQzrBrT+qEd/qvJtAVP8z16ujq5quXRd6vVpX7XiOyeNOvc/q7J3Uq7bPjup/9u9zEZCRaiHEJa2w3GizV+awd9eQlFsKwB12fzLFfimfmMaywHwFC+7oSf8WAcApSZcqimDtW7BplmoUHdxUz3Fga9j7A1QUqF7gQc9An4frlxnTUKoC8v2LVS/00GkqydglYuHBhby57U0AnOycuKfDPUxuOxkHu9NvP1JQUYDJYsJOZ4ed3o788nxrMG62mHls9WOsTF4JgL3enl4hvUgrTsPRzpHvx35vvU5eeR4+zj5sSdvCo6sfpchQhLuDO7+N+40AV/X3+NTap1iSsASA/mH9eb3f6xwrOEaYexjBbsEAxOXFsTRhKbe1vQ0vpzM06vUkbVPjkvoUl4tSg4kxM9eTXljO55O6EZdRxIu/H8TFwY6/HulPpH/N4GV7Yi7fb0/mrv7RtAiqymz81aZEXvj1AAD/N6IV9w9qDsCW+By+25bM2zd0rHUvaFDt4Jq4LMqNZsqMZoorzCzZm8am+BwARrYLZvatXRv769d2I3BsheqQDusKQe1VO5sVp0ahU3bUPEenrwqm29+ggtCf71NTqu2dYewHKgjd860q4+ylgunud6mAMmUHfD5MXeOmb1XSrk/6qXW4k36FqIHw7QQVQPs1h1t+gD2L1PKvkqzav0frq2DcrDNnntY0NZp8uja0OAsMRaoDv7rSXPhskArgQU3lnvzHmUeYRaOQ6d9CiP+05NxSXl9yiNiMIpZNHYBDta1GknJK2b/qW0btr8osnBk0EM+bPsXZp9p6K4tFTQn758Wq9Tz2zjXXD4V2UQ1lYOvz+I3+fetOrGNV8ipic2Ox09txbYtrGRU1Cke7qpEITdN4dfOrbE3fSmJhIgDNvJpxd4e76RHSA38XtTbPaDay/PhyFsUuYmfmTpvPcdQ78svVv9iMch/KOcT7O99nY2rVvsf2Onu23rIVBzsHTBYTY34eg4+TD4dzD2PSTHQM6MgHgz/Az0VNNcsuy2bMz2MoN5XzcJeHmdx2cq0j3Y+sfISVySvxcPDgoS4PMSFmQqPUn7RNjUvqU1wunvtlHws3JxHs6cyyqQPwcLbnls+3sCk+hzYhnvz2YN8a22dVTsG+Z0A0z4yybYsWbD7O87/sx40yFgwpp8vg685pJtSO43l8vOoojw5rWes67tOyWFTncdputWa3JEtlfK7cIvJUmgbxq1SCzuqBs6MHhHVWU6pN5Wo69cg3IaTjydHeH1TyK1c/GPOeyhANqhP7h8lVM8sqdZmkOrTdTllLvvwF2PCBGr32jYakTWqa9I0L1fuluSrQrr6mF8AzTN1L5VRosxHaXQe9H6jf9pT1lXEAvrwKPEJg4s/gHnD+PktYSVAthPhPKjWYmL36GJ+ujcdgsqDXwcI7e9KnWbXGMvsIzBmism9G9lcNs7kCXP1Vb3ZJ1sltG3ZVrX/2jYYrp6v9DbPjVAOftgf8W0LX2xu2b+MFomkaZaYyXB3OkIEcmLlzJnP2zbE55uvsS+/Q3jzW9TECXau24tI0jT8T/uStbW+RW67Wm/UO6c1nw9Ua8fn75/POjndq/RxHvSPP9nqWa1tcW+O9benbiM2NJdIrkubezQlyDUKn07E3ay+3Lb0Nk0VNSxwZNZJX+r6Ck13V9LCCigJ+OvIT3YO60z7g9GvTVySt4OPdH3Mk7wjP9nyWm2JuOmO91JW0TY1L6lNcigwmC4t3nmBo66DT5vLIKqpg0Ta1XKKw3MRna9Xa2YV39LQmDUvOLWXE+2spMagMzQvu6GGdWQWw7EA69yzYQYCHE/1b+HNHvyib/B+fffM9ww8/R6Q+A0PLsTjevPC8fN9aFWWoNb87vqzKO1JJp1cjxAOfqhqdtVggYTWsfQeOr1fHHFzVOuTU3baZs5sNhas+rJomXXl+2i7wiao5Ums2qt019nwDoZ1h1DsQfprRdmO5CporM2zbOcGDW9WodaXjG2H+GDWCHd5dJeRqPfb0I83nm7FczZw7l+21LiImiwl7/cX9fCVBtRDiomMyW8gvM+LvfvokYnklBvalFODr5ki7MPXAkFti4MZPN1FhspBXaqCoXAVavaP9mHZVG2KCq/1eqCiCOUNVko6mfeC231SQ/dOdak3SqRzdYcCTajuL2tbzXAI0TcOsma0N07oT67h/xf10C+rGHe3voG+oypL5y9FfiPGNobWfGuXYmbGTdSnraOXTipTiFL45/A2ZpZkA9AzuyUdDP8LZ3nYbloKKAubtn8eG1A2MjBrJlHZTAMgpy+GWJbdwdbOrubbFtQS6BmLRLJg1MzqdDgd9/R9AMkoy+CHuB3ycfbg55uZzWiNt0SysTFrJgPABNiPx50LapsYl9SkuRW8tO8zHq47RPdKH7+/pXevvqUNphYz8YJ3Nsdv7RjJtrG1CrcrkYgCL7u5Fz+iqBFAGk4Ve01eQW2IAIMDDifVPDcZJD2x4H23V6+gs1dZGT/oNogc20rc8jbzjatulnQtUxzWo2V5B7dQSqrJ82P+jOh7eHUa8AQlrVQbuvER13M5R5Snp/xi4B6otrTIOqAzaHsEqA3d9f/drmpomXbn3cS2MZqNaxpS0Bb64EtBgwP/V3LYK1G4fFrP6TueRpmnM2DaD44XHeb7X84S418xkXmgo5Pn1z/NIl0eI9o6u5Sp1V2os5UDOAUqNpZSZyyg3leNs50ynwE7W5VONSdM0kouSbXbneHjlw+zK3EWIWwgtfFpwd4e7ifCsPTFfpYKKAranb8deb08bvzbW5WCV8srzMFlMNY43lATVQoiLgsWisSUhl9/3prJ0fzrdInz4bFK3GuVySwzMXn2UrzYdp8JkYUKPJky/ViUGKSg10vHlv61lw7xdeG50a0a0C7Z9gDGb4Mfb1d6PHiFw9xrwOLmvp7Fc7ZEYv0aNPldu2RDS4dyTb/zLUotT+WTPJyQWJpJZmkl2WTaT2kzi4S4PA1BsKKb3t72t5Vv5tCLMPYyVySuJ8Izg+zHf1zqKbbQYWZ6opnC72Lvw5oA3z7gG+dQ9SC/aPUnPE2mbGpfUp7jUFJYb6fBiVds07/buDG4VWKNcWkEZM1ccsf7s7+7EA4Ob17oN1jt/xwHw+PCWNX6fvvT7AeZtSATg7Rs6cn2MC/xwGySqgN3UehzppTrCj/8MgW3gnnVnnmVVnAU5R9WIrkM99rGuKII1M9Q2SZWBfHh3NSLd/Arbz9z/E/z+qMpNUp2TJ3S8Cfo+Al7hdf/sRrD2xFqmrprKLa1v4d6O9+K2Z5GauTbiTbXe+jwpN5UTXxBPuamcjgEdsdPb1Siz+Mhipm2chp+zHx8M+YCOAR1t3n9u/XP8euxXQtxCWDhqoc2Mskqnhnana5e/OvAVb21/q9b3wtzDuLP9nVzf8noAdmTs4KNdH+Hq4EqUZxTR3tFEe0XT3Ls57o622bjNFjMHcw6yI2MH2WXZ5FXkkVueS0JBAjllOay+cTVuDuq5a/zv4zmUe8h6roPegcltJ3Nn+zttnlNic2NZkbSCDSkb2J+zH0vlenogwCWABaMWEOauZjPcuuRWYnxjeK7Xc7V+t/qS7N9CiAsqp7iC77ef4OstxzmRV7V11b6UAoxmCw52evJKDLy+5BCh3i7MXZ9A8ckMpJF+rvi5VY0auznZ8c1dPfHL3kbo3lm4mQvQbwQ2opKMlBeqHvHKRlvvoPafrAyoQT0wDHvxvH/vhrBoFtanrCc+Px5ne2dc7F1wsXch0iuSFt4trA2ipmn8Hv8707dMp9hYbHONTambrEG1u6M7f137F98e/pYf4n4gNi+W2LxY7HR2XNP8mhqjz5Uc9A6Mih7FqOhRdbrvUxvqyymgFkL8dxSUGTGaLWecRVWbhZuPW/97xnUdGFBtuna50cyxrGLahnoR4uVi7SQ+E51OxxNXtjrt+7f0jODbrUn0jPLjurae8NVVKhh0cINRb2Hf6WbCy/Lgw9WQeRC2fwE97679YmX58PkQyE9SWztF9oPmQ1WyrepTravTNBUk//1c1dKpqIEw4Am11Kq2NqDddSrgXnyP2vu4SU+1zrrNuHoFsDllOSQVVe04oENHC58W1uCsPnqH9GZYxDDmH5jPkvglPN7tcUZ2ndnobVipUSVNrQwON6Vu4uFVqp1u69eWZ3s+W2PpUu+Q3vg6+5JTnsOUpVN4ue/LZJdlM7jJYJp6NuXxbo+zJ2sPiYWJ3PfPfcwfMR8PR9vEaPMPzOfdHe8CoNfpaeffjn6h/egb1pdWvq2sy6gmxExg+fHlGC1G67NHbnkuh3MPk1KcYpOjpNxUzvaM7QCsZa3N50V6RvJwl4e5IuIKAF7f8jrfx31PbVzsXTice5iuQWpK/twr55JanEpqcSqLYhexIXUDc/bN4bdjvzFvxDzr7iB/xP/B/APzrddp5qWyqycUJlBoKCTItep5r7l3cwpO7cT5F8hItRCi0b29LJbP1sZjMKueRE9ne0a2C2FMxxB6R/tZE7D87+d9JG5bQpgum5/N/WgZ4suTI1oxqGWAbeNWmKa2ttj3w9k/3MENRr8DnRonGdX5ll2WzR3L7iC+IL7W9/uG9eWTYZ8A8PyG5/nl6C8AdAzoyK1tbiXINQh/F38CXAJqDZYLKgr49vC37Mnawz0d7qFTYKfz9VUuO9I2NS6pT3EhVJjMXPneWnJLDPzz+EACPeo2YltuNNPvzZVkFxt454aOXNfVdrR19upjzFh2mHsGNOPpkTGnv5DZpPYAPlPG6GqKK0w4Y8D+2/FqhNrVDyb/aZtIc9vn8Ofj4OwND+2suY+wpsFPd6gAuXoWbQAXH7hvE3ieMvXYWAbf3QzH1M4N+ETCyBlqi6u60DQoyW5Qcq2CigJGLR5FoaHQ5rinoye3t7udm2NuPmsOEU3TqDBXWNvJtSfW8sbWN0guSgagc2BnxjYby+Amg60JOGtTYa7gi/1fMKHVBLydvWu8b7KYOJx7mMVHFvNn/J/c2/Febm93OwCJBYlM+msSFeYKSk2l6NBxbYtrcXVw5fa2t1unK5caS3lq3VOsTl5tvW6IWwiLr1qMu6M7J4pOMPGviWSXZdM9uDudAzszOno00V5qOvi8/fOsQfWpAlwCWHb9sjMuySoxlrAncw8tfVta6yK7LJtt6dsorCgkoTCB+Px4jhUcsy4Z+2jIRwxsopYbLIlfwiubX6FnSE+aeDTBx9kHHycf/Fz86BbU7bR/V5qmsTJ5JTO2ziC1JJVPh31KnzCV5G5T6iZ+jPuRvmF96RPaxzo9vdRYSnJRMq18qzqjzrQ9Z0PI9G8hxAUzb0MCL/1+kI5NvLm1Z1PGdgytMc0NIGHnPzT97QbssFDo2RL36z5CH9GzqkBxJuxaCOveAUMxoFM93K1GVesR14Gzp3oQcPFV22Zc5EnFykxluNirzKyapnHjHzeSXJRMv7B+mCwmykxllBhLOJhzkIltJjK161QAfj7yMy9vepn7Ot3HlHZTLvrkHv910jY1LqlPcSH8tOMEj/+wB4CnR8Zw78BmNcpYLBr6U7alWrApked/PUCYtwurnxxk3YGizGAmq6iC0TPXUVRh4t3xHbm2yxmmN88bBen7YdIvENbl7DdsNsH3kyD2T5Ule/Lvavq2zQ2b4dMBam/mblNUhuzqdn+rtqrS2cGUZWoJ1NF/1Mh2XoLaOnL4K7bnbPoYlv1PrZnu95iatl2fKePnaHfmbqaummqdalxiLCG7LBuAQNdA/rzmT5ztnTFbzGSVZVlnfDnoHdDpdMzePZvVJ1Yze9hsfJ1VcrMKcwXz98/n832fU25Wu37o0PHt6G9p69+2xj0UG4p5eNXDJBQksPKGldbO/28OfcP2jO3E58dzvOi4NcEmQL+wfsweNhuoWiKVXZbNezve47djv1nLxfjG8N3o76xTws0WMx/s+oB5++cB8FT3p7i1za3W8odzDzN56WRKjCUAXBl5JW8PfBtQzxjlJ3cxKTYWszVtKxtSN7A5dTNFxiLeH/w+Q5sOPae/j0q55bkczDlIh4AOeDqq39tGsxF0NCiXSuX9f33oa5p5NWNw08GNcp/nQoJqIcS/qvp62sJyI4nZJXQI9z79CSXZKutmUVq1nnKdegAI765GpeNXq4ybAGHdYPTbNR8eLgIV5goO5hwk0jMSH2cfADakbGDmrplEeEbQyqcVMb5qpOLnoz+zKXUTy65bZn04iM+PJ8A1oMYUrmJDMUaL0XpNTdNIKko6axIP8e+QtqlxSX2Kf5umaYz5cD0HUtUI6HVdwnlnvO0a1vSCcq6bvZEADyfevqEjzQPV7+2l+9OYsSyW23pHclufSABWHMrguV/2U2GykFtioF2YJ7890K9GQG6Vdxw+ODkt3CME7loJnqFnumH45X6V2drOCW79CaL61142cT3MH63a16s+gg43qg7n3Hj4pL/qqB7ynErUWSl2KXx7owrWH90PLt7quLEMPuiotp4c+4Hq3K6n7LJsfj7yM0fzj5JQkECP4B480f2Jel+nktliZknCEmbtnkWv0F5M6z0NUNPEB30/yFpOr9PjbOdMqUlNxX6176tc3fxqm2ull6TzR/wfrDi+ghPFJ1g1fpW109pgNuBo50h2WTb3/3M/h3IPEekZybwR8/B38ceiWRj8/WDrrhigpjgPCh/EDa1uoFtQt9NOK9+VuYvXt7xOUmESbw98m/7hNf8uN6dtRoeOniE9a31v6qqphLiF8GCnBxkaceZA2WQxUWwornWEXZyeBNVCiEZnNFt44df9rI3LJszHhUGtAhjYMoBSg5k3/jrMnEnd8HWrQ2Zlixm+vl5NI/NvCbf8CGvehN1f1ywb1hW63wkdbrqotpBIK07ju9jv2JGxg4M5BzFajLzW7zWuanYVABtTNnLPP/ec9vy3BrzFiKgR/9btivNA2qbGJfUp/m2b43O46bPNACy+vw9dmvrUKDPt1/18uUmtnXZ20PPCmLZM6NEEnU6H2aJh0TTrKPWauCxu+2Kr9dxv7jplu8dTVU7TrhTSCW7/6/RrjY+tggXj1AjzjQsh5iz5L36coqZ4g5qu3Xcq7FqgtoyM6Au3/Q7Vk2VZLDC7D2QdUjlI+j2qjm/5FP76P/BqoqaT2zuSXJTMlwe+pENAB8ZEjznjdNvc8lxuXXKrdao1QPfg7nxx5ReAGtncnrGd3qFVCTbLTGU8vfZp7u14r3XHitoYLUbKTeXWTunU4lRG/zzaZrQY1Aj0A50e4J6Op2+XAYoMRdZrlZvKGf/HePqF9WNN8hqSipLwdfZl9rDZtPFrY73Pefvn4e7gbk3eFewWXOfpx5qmYbKYVCbyBjCajdjr7SWnyXkkicqEEOes1GDiYGoh3SLVVCkHOz37UgpIyS8jJb+MrQm5zFgaay3/3vI4XhnX7uwXXveOCqjtXeCGL8EnAsbNgo4T4J8XwVACba+B9teDX82peI0trzyPPVl72JO1B71Oz6DwQbT1b1tro5hRksGcfXP46chPNo22r7OvdboVQFv/trw/+H0SChKIzVWJwgorChkWMYzrW15vHbkWQghxYXyxPgGAm3s2rTWgzigs59ttKhBsF+bJ/pRC/vfzPtbEZfL2DR3xcHbAjqpgZmDLAPq38GfdkWyGtQ48c0ANcOQf9WeX2+DwH5C2W03Lvn5+7Z3IcUvVn51uPntADXD1xyoL+OZZagurP6aq485ecM2ntgE1qM/s+4i6h82zoed96vj6k9PH+z0K9qrjvNxUzo9xP7IodhHfx37Psz2frTX4rTBX8MjKR0guSibULZQbWt1AlFcULbxbWMt8fehr3tnxDm392loTj2WXZRNfEM+h3EP8ec2fpw06HfQOODhWvRfqHsquibswmo2Um8spN6mXs71znbZYqj5j7J+kf0goSCChQP07CXMP49MrPrWZLeZi78L9ne4/63VPR6fTNTigBs7pXNG4JKgWQtiwWDQWbU/mz71pbE3IxaJp7HrhCjyc1S/uJ6+MwWLROJFXyurYLDYey6HMaGZAywD+N+qUBrUkWzXGCWvA1V9lFHX2VmuzAMa8C0FtqspH9Ye7Vvw7X7SaN7a+wZKEJdafP9v7GYEugQxqMojWfq2tW0pkl2Uz5ucx1rVXPYN7clXzq+gc0Jlwj3CbnmIvJ69GW7MkhBCi7j5edZQyg5lru4QRHeBeaxlN04jyd8PD2Z4pfSOtx3OKK3B1tMfF0Q5nezvu6h/FobQiPp/UjbnrE3hrWSyR/m7W0elTvTu+E99tTeLmnk1rfd/KVKH2bAbofofaXurLq+Dgr7B6eu37JR9Zjgbo6poczMFFZebudR/s/Ao2zFRTuK/6ELyb1H5O++th5atQeAL2fqe2zCpKA49Qjkb1pvnJYi18WnB9y+v5+cjP7Mnaw01/3sQNLW/g7g53W7d5smgWnl3/LLuzduPh6MHsYbNr3Vu5zFSGvc6eAzkHbG9f78D0/tMbFDg62DngYOdQY1lVfYyJHoObvRtvbnsTHycfPhjyQa1bWAkBMv1bCFFNVlEFj32/m3VHsq3Hwn1c+GxiN9qEVvt/z1ACSZsgYS2WhHWQvh9dSAd0MaMhZgx4BMOmj1TwbCiu5ZOAzhPh6o/O8zeqXXpJOj7OPtZtJRYdXsS3h7+lY2BHSowlrDuxzrr+ytPRkw0TNljPfWz1Y+SU5fBg5wfpHtz9gty/uDhI29S4pD5FY+n35kpO5JWdffo1Kot3ZSLN15ccYt6GBF65uh039agKiqvnDNl/Ip9//vyOLt36MqBr+1qvaaMwFVJ3Q8sRtqPPlVO53YPg8ViVfHPXQvj1AUAHU/eCd7XAPDeedXN6M83fjwEtxzGt32v1n/JrMkB5PrifJTDcNAuWPQO+zVTwX3iCjQMe4oETf7Jo7CJa+rS0Fs0oyeCd7e/wV+JfgJpm3SOkB+8Peh+TxcSUv6eQUJDAZ1d8dsY2M7komQPZtkF1O/92hHv8u3tY16YyVJIp1pcnmf4thKiX9UeymbpoN9nFFTg76HloSAuubBtMswA324ZkzyI1hezk/ovWR4SU7eq14iWwcwSzQR0P6QR9HlI/F5xQLwdXGPpCo3+HjJIMliQs4YqIK07bEK9IWsHzG55ndNRonu2lRgLGtxrPjTE3WssYzAa2pG1hzYk1lJnKbM5/te+ruNi7SOMqhBAXobwSAyfy1O/tdmFeLN2fzrojWbw6rl2tv7er70zh6+aI0axma1UPqquf1y7jF9qlPQUr/KHJH7ZbWVVnMas10yteVp3L1dcog8q2DdB8WNVuFp1vVZm5j6+Hvd+rUeaTftr2AS8FBaDpdPie3E6o3uwdzx5QA3SZpPKc5B4DwOgexPSC3Zg0E0sTltoE1UFuQcwYOIPrW17Px7s/ZmfmTnLKcnBzUM8OX434ir3Ze8/aCd3Eo4l1T+KLjbT3oi4kqBbiMmS2aMRnFdM80B2dTkduiYG7vtpOmdFMyyB3Prq5Cy2DTpkypWlqStqaN9XPXk0gaoB6BXeA5M1w+E+IX6MCaP+WKrNo66uqbX91/sTlxXHfP/eRW5bLgPABp9y6xt7svSw8uJCliWpN2oGcA9Z1Vqc2mI52jvQP719rJs6z7YUphBDiwtmXUgBApJ8rReUmHvp2J0azRoCHE0NiAtV+1IcyGNU+hN7Rfja//6/tEsZby2LZlZRP3zdW8tb1HejdrFqZ8kI1NRqgNBvmj1HJvqovYwLIOAi/PaQ6mitt/BC63wVOJ6ejH1mu/mw+zPbcjjedDKoXQf/H0YCPdn/EZ+krQafjCrdI7ut43/kN9JzcocfdsHYGAF+1HkBi9hb8nP2sey6fqkdID3qE9CClOIWs0izr/bk7utMntM/5u1chLhINSqU7a9YsoqKicHZ2pmvXrqxbt+6M5T/++GNat26Ni4sLrVq14quvvmrQzQohzt2upDyGv7eGK95bS16pEVC988+PacOEHk349YF+NQNqYzn8dGdVQN13Kjyy92RysZvUA0W3KWp7j/+Lh3vWwX2boM3V/0pAvS19G5P/mkxmaSZBbkHWJCKapvHlgS+Z8OcEbl1yqzWgntx2Ml+O+BJn+39vj00hhBBKbomBzfE5lBpMNGgVYvZRmNkF1r5d463KoLp9uDdh3i68MEYFvO//c4SrPtrA5HnbWLg5iZvnbOGPvWk25wZ6ODMkRo3kpuSX8cbSw7YX3/A+lGSpadEhHVVg/eVYFUSbDBD7F/xwO3zaXwXUjh4w6m3wjYbSHLUPNEB+EmTHqu2ump2yD2+bq9Ve0NlxaCk7eX7D83y29zMA7s0r4J2+r1vXGJstZutpZouZ3Zm7T1tlFs3CmuQ1Z6xWGz3vAVc/0n2a8mn+XgAe6/bYWdcoh7mH0SmwU90/R4j/iHqPVC9atIipU6cya9Ys+vbty6effsrIkSM5ePAgTZvWTMowe/ZsnnnmGebMmUP37t3ZunUrd911Fz4+PowdO7ZRvoQQokq50YyTvd7aS1xuNKM/+d8frIhj9upjWDRwsteTUVhu3QJLJVU55f9hi0Vl6V79utqCQ28PY96HLhNPfwPOnhDS4Tx8s9otS1zGM+uewWgx0iWwCzOHzLTuLxmbF8u7O97Follw1DsyMmokt7S+5YzbcwghhDi/1h3J4pHvdgMQHeDGRxO62ObtqIXBZMHBTqfatm1z1NTkla+Am7/Nvsn7TpwMqsPU9Sb2juREfhkLNh3H09kBHzdHfN0caBnkwZVta06jHt+tCcsPZgDw8JAWVSPCBSeqkmxe8TJE9oWvxqmM3fNGqgC5rGqvYlqNhtFvq32nHVzh1/th40y1RWTlKHV4D3A5Jeu4sye0GgUHFrNpx8f8mr8NO/S8kJXFtTovCFY7bGxM3cjrW17n9X6v08y7GU+tfYr1KeuZPWy2zdZUoLZd+t/6/7E0cSlP93iaW1rfUuN7my1mlh9fTqGhkCsjr8TLzR8e3M7bm1+iLHklnQI6MSZ6zBn/joS4nNU7qH733Xe54447uPPOOwF4//33WbZsGbNnz2b69Ok1yi9YsIB77rmHG29U6xWjo6PZvHkzb775pgTVQpwjTdOwaGCnV41+QamR2+ZtpX8Lfx4f3gqAuesT1MOEiz1xGSpp2LhOobx4VVu8XU+zp3RJDuxeCNvnQZ7aSgJnL7UvZtSA2s/5l+WW5/Lxro/5Ie4HNDSGNR3G9P7TbUafY3NjaeXTiuGRw7m2xbX4OvtewDsWQggBsCsp3/rf8VklfL4unndv7HSG8nlM+kK1bbMmdFYZsiv98Rh4hVunUVtHqsO8rUWeGdmaZ0bWrTN1cKsARrQNxtXRjqGtq60/XvEKmMrV/s4xo9UsrEm/VAXWAG6B0P4G6HADhHauOrfDeFjzhhqh3jEfEk/O8GxxytTvSh1vggOL2ZKyEdwcuMGlCdcWJ0Lna62zv/449gfHC48zbeM0AI7mH8XJzokiQxEA+7P380PcDzzW9TH+b+3/sTF1I/Z6e/yc/QAwWUxsTttMv7B+bEjZwNvb3+Zo/lEA/Jz9GBoxlM0FcSxLXolep+fZXs/Wee9lIS5H9QqqDQYDO3bs4Omnn7Y5Pnz4cDZu3FjrORUVFTg7206xdHFxYevWrRiNRhwcZH81IRpq6f50PlhxhOfHtKFVsAcT527lUFohCdklTOwVQYCHE3/tTyOrsAStsIAgVz+mXdOZUe1Dar+gsVztZ7n2bTCWqGNOXtBpAvS6X+0nfZHYlr6N7+O+B2BCzASe6v4UdqfsuXl186u5uvnVF+L2hBBCnMaupDwA7uofxZx1Cfy2J5UnrmxFqLdLjbIZheXc+eV2ispNLNmXTlr71YQUpYGTp8qove97+H4yTFlKsU8MhWVqWVPbsHpkkE9Yp6Z1t70Gezs9n0zsavt+6i61vRTA8FerljW5+MCkX2HnlxDUDqIGgl0tj9Z2DtD/cfj9ETWFvOLkrhjNr6j9fpoNAVd/Hs1MY8TV7+G14eQIeYuq8k90f4K1KWutgbC/iz8zB8+kfUB7ykxlPLTyIbLLslmWuIwSYwku9i68P+h9+oT1QdM0pm2cxm/HfqO1b2sO5R4CwNnOmZY+LRnUZBCg2lmA8S3HE+MbU/f6FOIyVK+gOjs7G7PZTFBQkM3xoKAg0tPTaz3nyiuv5PPPP2fcuHF06dKFHTt28MUXX2A0GsnOziYkpObDfUVFBRUVFdafCwsL63ObQvxnbTiazfqj2fzflWoUetbqYxxOL+KWz7fg6WxPYbkJf3cnFt7Zg0BPZ8g+wi8t/8ZY/A0uFdlgAZb4wrpg8ImE8G7QpCeEdoH4VbDsf5CXqD4sqD30vBvaXQeObv/6dz1RdILFRxbzR/wf5Ffk42jniJ+zH7+OUyMUwyOGM77leEZGjaRbcLd//f6EEELUX7nRzME09Vw3qXck+1IK2Byfy7wNCTw72jbhV4XJzL0Ld5BTonaTWDq1PyG7XlNvxoyGsTPVHsqJ6+Cb8bjf+Q97pg0nJb8MT+dqgzapu+Hocuhxj5peXV3qLvjqatDMkL4Xhk6zzQViscCy59R/tx8PYV1sz3fxhr6P2BzKKs3i12O/cnPMzdbklr+4u7MltAl3ZqXRzGjC4hZIoosHB+P/wNXelSFNh1RdwM5B7Re95RNa7/hGrSHX20P0IGsRX2dfnur+FM+uf5YY3xhmDplJ8Mms4C72Lrzc52UeXf0oJcYSvJy8mPX/7N13eFRFF8Dh3/Zs2qYnpJHQe++9SRERUSkWFAQVEBCxgKgoWGiKKAqCDVFQQVTkE0RQem/SOwnpvWez/X5/3LAhJEACoc/7PDzJ3jJ3dkXunjsz53SdRwP/oqVZga7yd/njGcdRK9UMqjmIEQ1HYNAZnMeMaTyG1pVaU927OoIgXNk1Zf++NOPgxfX7LvXWW2+RlJREq1atkCSJwMBAhgwZwsyZM1GpVKWeM23aNKZMmXItXROEu0J6nplpa04gSXIG0wg/N/LNNiavPIrF7qB6gDsPNwll8TMtmLP+FD/siiHHZCPY4MKSZ1sRaT4JX78OsTtRc8n/6AUZ8p+UY3BytbxNoQTJIf/uHiSvF6vfv3hNzZtkc9xmlhxfwo6EHUgUJbApsBWgVhS9E4VCwVut37rp/RMEQRCu3dGEHKx2CT93LaHeep7rUIWd5zL4cXcsY7pWdwbDkiTx1u9HOBCThUGv4Y/RbansrYejv8sN1e0nl4ga+D183R3STsHSASiHriHM56JkWikn5GRi5hyI3QOP/QgXZjVZTfDbSDmgBtj6Maj10GmC/NqYAb89L2fjVunKVApy9bnVvLH1DWySDR8XHx6u/jAOycFXxxZxXqfgf6HB1DJbiNHpMf7xEABeOi/q+dUjwFWebm51WDHWfgDDri8gTh4tJqylvAzrIn2q9qFZYDP8Xf2duUQuaB/ani+7f8kfZ/9gcJ3BVDFUce5TKBSMbTKWMI8wjqQd4em6TxPuWTIvEiAeWgtCGZUrqPbz80OlUpUYlU5JSSkxen2BXq/nm2++YcGCBSQnJ1OpUiUWLlyIh4cHfn5+pZ7z+uuvM378eOfrnJwcwsJuz9p1glARknNMxGUaaVpZXvPr4aJh7dEkck22Esf2rBtE7wbyDA9vNy1T+tZjcOvKrD2azMNNQqgkpcE3j8qZRhVKqN5drn1ZvTtY8iE3SX6yn3oSYnfJf3IT5drSrUfLU9QulPy4Bf539n9sT5CXk7Su1JpHazxKbd/aWO1WHBcCf0EQBOG29s3WKH7eE8unjzWmZlBRkHth6nejMG8UCgWdagRQPcCd0yl5/Lgrhuc7VgXgpz2xLNsbh1IBnz3emMq+bnB+O+QlYdd6IkV0lL/E6r3hieXwVTdIOgy/PAODfpSnYeenw48D5YAa4PRauSRWN3kdMhs/gNTj4OYPzYbJ6543fgAaF3nt9PIhkB0rB9R9PwOvK38XTStI472d72GTbDTwb4C/3h8ApULJjA4z+OrgAtbHbuCETgvYcVG54OPiw5fdv3QG1AAbYjYwaeskHg+JZHx8YV6TS0tvFarkfpnlXEDjgMY0Dmh82f39qvejX/V+V3xPgiCUTbmCaq1WS9OmTVm3bh39+hX9T7hu3Tr69r3yukWNRkNoaCgAP/30Ew888ADKy4yC6XQ6dDpdebomCHecc6l5rPwvgX9OJHMkPocwHz2bX+2MQqFAq1YytW9d4jIKOJ9hJDotn6QcE73qBTGxV21nYrILqgV4UC3AQ37q/u1gOaAOqg+PLwfPi264ah24+sglsKp1hdaj5PrTOfFydlLXG5vIS5Ik/on5h/0p+wl1DyXSEEm4ZzgeWg88tfKUvNdavEawezCP1HiEMA/xME0QBOF2lZxjwtdNi1pV/Pvcd9ujmfq/YwAs2HS2WBKyA7FZADQO9wJAqVTwbIcqvPbLIc5nGJ3HXahM8Xqv2rSvLgenF0apfytohPW/FB5rUTi66h0Bj/2M+ate6E7/Tf7K8bg9+CEsGywvafKqLOcF+WsCbJ0t3x89Q2Dbp/L5fT6FWvfL067/fRfWTQaFSh7B9qkCAxbL5yBnyV5+ajlNAptQw7tGsfc9c/dMcq251PGtw+Kei4vl+ajrW5ePu3zKuTN/cSr6H6o2Hk6kd9USI8wAy04uw2w3o/avDxeC6uqXWX8tCMJtodzTv8ePH8/gwYNp1qwZrVu3ZuHChcTExDBixAhAHmWOj4931qI+deoUu3fvpmXLlmRmZjJ79myOHDnCd999V7HvRBDuEPlmG5/+c5qvt0Zhc8jTmxUK8HPXkWOyYdDLU9/6NQ4tX8OSBKtflteH6b1h4JLiAfXlKBRy5tQyyjZns/TEUtKMaVT1qsrjtR8v03lpBWm8u+Nd/o39t8S+buHd+Ljzx4CcbGVc03Fl7o8gCIJw8605nMjIJfup4ufGxF61uK9OoHMpYIca/gR5upCUY2L1kUSm9K2LR+G07hc6VaN5ZW9aVy2ardi3UTALNp2lekDRLKlQbz0vdavB8PaR8gaHw5n1+097S46tP8VDjULQa+XANd2rHpMso5ivmYPboe8geR8kH5ETmj2+DAJqyaPOOz6DlS+Aqx8gQcPH5YAaoMMrYC2ALR/KAXWdh+DBucXWYa84vYL3d72PXq3nsy6f0aJSCwC2xm9lTfQalAolb7d+u0TizAuqVOtJlWo9S933z/l/WB+znl1Ju1Cg4NGWL8OxzfI9OrBe+f4DCYJwU5U7qB44cCDp6elMnTqVxMRE6tWrx+rVq6lcWc4KnJiYSExMjPN4u93ORx99xMmTJ9FoNHTu3Jnt27cTERFRYW9CEO4U286k8crygyRmmwD5i0efBpXoXCsAP/frnJ2x71s48IM85fvRb25Ipu6TGScZt2EccXlxALQPaV8sqDbZTMVKWl3so70f8W/sv6iVah6o8gDZ5myisqOIy41jX/I+Uowpxaa/CYIgCLcnSZKY+6+cdfpcWj7Pfb+PlpE+vNm7DvVDDUT6ufHXuPY8+sUOzqTk8eehRAYVjirXCfYsUZNap1axdlwHLPaiJT51gw3UDb5oDXHsTshLQtJ5ck7XnORsM99si+KFztUAuZTWWkdzvtANZZTlGzmgvnA/DCjMXN1tipxP5Oy/kB0DHsHQ85JysF3eBN9q8qh1vUeKJS2zO+wsOroIkPN8jPpnFB93+phmQc14b+d7ADxR+wnq+BZPuFYW57LO8dLGl5y5RDqGdiS4UlMYvRs0+uLJ0wRBuO1cU6KyUaNGMWrUqFL3LVq0qNjr2rVrc+DAgWu5jCDcdVy1KpJyTIT56JnyYF261Co9F0GZGDMg+SikHJe/JBz4Qd7e5S25HEcFW3V2FVN2TMFsNxPiHsKDVR8k0iCPIEiSxBcHv+Dv83+zuNdiPLQeJc4f33Q8qQWpvNrsVWr61HRutzqsqBQqUf9SEAThDrE7KoNjiTm4aJQ81TqC77ZHsysqg11R6dQL8UShUODlquXRpqFMX3OCZXtjnUH15ahVyhLTyIs5+hsAiloP8GLluoxfdpBP1p+mS60Aalfy5EhhfeoTEYPBzw12LYQe7xefNq1Sy0H2V93kaeF9P5Ozd19MoZDLSJbi39h/ic2NxaAz0Mi/EZviNvH1ka8JcA3AYrcQ5BbE6Eajr/bxlaqKVxWG1B3Ct0e/BWBAzQHyDs/ga2pPEISb65qCakEQrs7ukFhzJJGYDCOjOslP0huHe7Pgyaa0r+7vnLJWLpIkP2HfvRBOrYWLsmMDUPtBaPfS9Xf+IjmWHD7Z94mzJnTbkLbMaD+jWNmNLHMWv5z6hZSCFF7Z9Aqfd/2c05mn2Ri3kZENRwLg7+rPV92/KtG+Rilq1QuCINxOTFY7k347TO/6lehau+TDXzedms41/ankpWfS/bV5uk0EH649ydx/z9CzXhCh3nIZqYcbhzBr7Un2x2RxJiWPMym55BTYaFfdr2RNaqnwflbaiKzD7pz6Td1+9Ksewp+HEvnnRApjfzzAqjHtOFwYVDcI84L270HnN+WEY5fSe8OIbXJd6iskHpMkiTNZZ4qVk/r5xM8ADKw5kBENRzD/v/k8XfdpDDoDKx9aSXxevLOE1rUY02QMSflJoJDvtYIg3DkUkiRJVz/s1srJycFgMJCdnY2np+fVTxCEW8xqdzDyh32sP56CRqVgy2tdCDKUPi26TGxm2L8Ydi2A9NNF270jwL82BNSWk6jUflB+El+BMkwZ9PilB2a7mRENRzCi4YhSR5WPpR9jyF9DKLAV0CKoBQdSDmB1WPmo40d0j+heoX0ShNuBuDdVLPF53h5iM4yM+GEfRxNy8HbVsGVCF9x1pd9XHA4J5UWJMy99DfD2yiMEeLowqHkYo5bsZ1dUBjMfacCA5hcFtOY8WDpAvtc9sbxk0sxDy+DXZ+WSUq+cAbWW9DwzPT/ZQmqumcGtKvPP8WQSsk389FwrWlXxLXb6zsSdrIlaw+hGo/F39b/sez+deZolx5cwouEI3t/5Plvjt/LTAz85Z1dlm7NZfmo5D1V7CD996RVsBEG4u5T13iRGqgWhgjkcEq8sP8j64yno1EpGdKyKXnOVUWlJkkeeCzLlqdsehSMDDrv8ZWLDB/L6LwCtBzR6HFo8C37VL9/mNUo1prLq3CqeqfcMAD4uPoxpPIZq3tVoE9zmsufV8a3DtPbTeGnDS+xO2g1Ap9BOtKzUssL7KAiCIFS87WfSeGHpfjKNVnzctHz+eBPcdWqMFhsJWSaqBRQvt3hpAH3pa4ApfeUEWza7g0Nx8mjyhczfTn++DOe3yb//PlIuiXWhQkzmeXk/QKsX5NrUgK+7jo/6N+Sln/+jfqiB73eeB6BucMkvvdN3Teds9lmOpB1hUc9FpS5RkiSJabunsSdpj5x5W6nGJtl4e/vb/HD/D6iVagw6A8PrD7/8BygIwj1LBNWCUIEkSeKdVUdZ+V8CaqWCL55sSudaV0m+lRkNq16EcxuLtgU3gaqd4eRfkHJU3uYRDO3GyQG1ruQXgutldVhZenwp8/6bh9FmpFlgMxr4NwDgqbpPlamNruFdmdRyEl8d/orBdQbzVJ2nnNlgBUEQhNtXrsnKc9/vI89so36IgS8GNyXES8+JpByGf7cXtVLBb6Pa8s22KB5rEV5y+vZVnEzOpcBqx0Onpqr/RcH5f0vh0E9yUjGlBk79Bds/kZcy2W3yCLU5B8JaQvuXi7XZoYY/m1/rTHxWAXWDPbHZJWeW8QvO55znbPZZAE5lnmLchnHM7zYfrUpb7Li/ov9iT9IedCodoxuPRqvUsitpF0fTj/LtkW8ZXn+4uJ8Jwg0iSRJYrSi02qsffJsSQbUgXIOYdCO/7Itl0+k0vnm6Gb6Fmbs/Xn+axTvOo1DARwMaXjmgdjjktdH/TAGrEdQu4F8TEg9Cwn75D8jT3dqNh5bPyxlAK4gkSRxMPUi2OZu0gjQWH1vMuexzADTwa4BefW3XGlRrEINqDaqwfgqCIAg33uH4bPLMNoI8XVg+ojUuhTOsQrz0WO0O4jLNDFq4k5PJufx5OJF/xne8cpCZfBQO/gh1H8YU0JCpq+S61Y3CvYpGtNNOF41Cd5oE7v7yQ+Z/3oXQFhC1GWJ3yWWxHl5Y6vImN52aGoEe/Dm2PQ5HyRWNG2I2AFDVUJUkYxK7k3bzzvZ3+KD9B85j8q35fLjnQwCG1x9OiHsIAK80e4W3t7/Npwc+5YfjP/BJ509oFNCoXJ+rINwqeVu2kvHD93jedx+Gfv1QqK6ey0eyWnGYTDgKCpBMJlTe3qg8Kn4g52LW5GTix7+M6fhxgia9juGRR+7IB1giqBaEi2QbrdglCR+3kk/Kso1W/jmRzC/74th+Nh2Qa0tfOLbAYmfTqVQApvatR99GIZe/kN0KPw6CM+vl15XbwYOfgm9VyE2G02vh3CZ5zXTrF0quL6sgQ9cOxeawOV/7uPgwrsk4+lbrK7JxC4Ig3EMuZM9uHO7lDKgBPFw0THmwLiN+2M/J5FwAnmhZ+cpfeo0Z8MMjkJsI2+diC+2AFN0RqEWEr5t8jNUEy4fID5UjO0D78fJo9fkd8sj1ssHykiiABz6W74dXUdr089q+tekV2YvWlVoT5BbEhM0T6FutLwA2h41vjnzDodRDpBSkEOYRxtB6Q53n9qvWj1VnV7E3eS8ZpgwckqNE+4Jwu5FsNlI/+4z0BQtBksjftJmM738g4LVXcW9bMgGeJEnkb9tO2ty5FBw8WGyfQqfD+4kn8H12OGpv73L1w56Xj2S1FLWl0aByL76ExLhvH3EvjsOelgZA4ptvYdyzl6C3J6N0lZP+OSwWjLv3YEtLRemiR+mqR6FzAcnhDP4dJjNqP190VaqgrlQJhfLmf4cVicoEoVBStokeczaTXWBlZKeqTOgp17X853gyCzefY+/5TOyFT8EVCmhXzY/HWoRzf/1KgPyPUssP/uHpNhHOupmXtfYN2PEZaFyh+3vQdGjR+rEbJNWYyvqY9TxWq6hUyON/Po5DcmDQGajtU5uh9YYWy+otCMLliXtTxRKf5631854YluyK4cGGwQxvX6XYPkmSeHbxPtYfT8ZNq2LHpK54ulymcoMkwc9Pwon/gd4HTNkg2QE44QgjODBAPrcgE9JOgqsfjNwGHkHy+ZZ8+LILpJ6QXzcYBA8vuGLfY3Nimb1vNsPqD6OeX70rHmu0Gp0Zuh2Sg4aLGzr3fd71czqEdijR9pNrnqS+X33mdpl7R46gCTePw2LBfOo0uiqRzqDwZrKmpJDwyqsYd8u5bdy7dsW4Zw+OnBwA3Nq2xa1NG7RVq6CrWhVbcjKpcz7BuHdv8YaUShQ6HVJBgfzSzQ2fp5/Ga+BA1AH+pf5/4LBYKNi3j7ytW8nfug3zyZMljtFVr4Zbu/a4tWuLJTqa5GnTwWZDV6MG7p07k/7ll+BwoK1WFd9nhpG/fTt5GzfiyMsr82eg0OvRRkbg3qEDAePGlfm8yynrvUkE1YJQ6PVfD/PjbjkZ2MxHGzCgmZyZ9Nf9cYxfJj+5qxHoTu/6wTzaLJSQS9aTSZJEdk42Xh6eVw6Qj/4Oy5+Wfx/wPdR5sELfx4Un6Q7JwbnscxxLP8ax9GOsOruKPGseC7otoE3I5ROOCYJQNuLeVLHE53l7S8o28eovB+lVrxKPt7xCzel9i+Qp3EoNDF8v14He9inSgR9Q2M2XHKyAJ3+Bat2Kb049CV/fB+6BMPwfcLny34c3tr7BH2f/oKqhKiseXIFKWfaSle/ueBeNSkNN75r0q96v1GPsDjtKhVIE1MJlmc+dI2vZcrJ//x17VhZqf3/8Xxxb5mnXAJLDQeZPP2E+eQp9g/q4Nm2KprI8K0SSJBy5uVgTk1B5e6EJKL680JaWRtZvv5Hx3WLsaWkoXV0Jencqht69sWdlkTZ/PhlLfwSrtdRrK7RavB8bhM/TT6Py80OhkR+a5W/eTMonn2A+drzYsZpKlVAHVwKHhD0jA1tWJvbMLLDZSm3/Sjx69ST4/fdRurqSv3s38S+/jD01rdgxKn8/XKrXwGE2IxUU4DCZUKiUKFz0KF1cUGi1WJOTsJyPcb5Hz969Cfnow3L351IiqBaEcjiXmsd9H2/G7pD48qlmtK7q6ywhkplvYeV/8XSpFUi472WeOtqtsOY1+cuEUgNe4eBdGXyrQ8NBENxIPi7tNCzsDJZcaDMWur97Xf2WJIkt8VsI9wgnwhABwMmMk/Rf1R+VUlVsajdAXd+6TGkzxVkeRBCEayfuTRVLfJ63wKm1kBMvz5aqiIAx9RQs6AC2ArjvXWg7tmhfbjLE74WLp1Abworuj5cyZYNKe9VcIiabiY4/d8RoMwIwvf10elfpDcBfUX9R1asq1byqiYBYqFCOggIKDh7EuHcf+du3U7B/f9FOjcYZ2Olq1iTg1Vdxa9vmin8HbRkZJLz6GvnbthXbrvL3Q+3ljTUhAUd+ftElwsNxbdoUl/r1MO7aTe4//zgDWl2NGoTMmYOuSmSxtiznz5O96n+Yz57BcvYcluhoJIcDr0cewW/kCDRBQaX2TXI4yF23nrQvvsB84kRRTflSqPz8cG/bFrf27XFr0xqVl5dznz07G+POneRt2Ur+li3YMjMJeGkcPs88U+yzsaWlkfjmW1hiYnDv1AmP+7qhb9iwTFO6JasVS2wclqhzqLy9cW3S5KrnXI0IqgWhHF5Yup8/DyXStVYAXw9pXvpBZ/+Ff6ZCaHPoOBHcCutgmvPkkecL66NLE9YSmg+HLbMh9ThUbgtP/XHNNaUlSWJf8j7mHpjL/pT93Ff5PmZ3mg3A7sTdDPt7GABuGjdq+dSijm8dGvk3omt413I9wRcE4fLEvaliic/zJrNZYEZlsBoxPbgA6vcvtpa6/O2Z4atukHQIqnSCJ3+74cuaAP6O/puXNxVlBa/sWZnf+/6O1WGlw08dMNlN/Prgr1T3rvgSlMLty5qcgibwKtVXrkByOEoEcQ6jkZw1f5H1268U/Hew+KisUol7p054DeiPW6tWZP70E2nz5junXauDgnBr1xb3du1wbdkSlZeXM5A07t9P/EvjsSUno3BxwdDvIcynTmM6dAjpkpFllcGAPTdXTnZ7CX3DhngNGIDnA71R6nRXf492O5LNVqZjnedYLFiTk7EmJGJNTECh1qDy9kLt7Y3Kxwd1QEDZgl9JQjKZUOorLgHvjSLqVAtCGR2Jz+bPQ4koFPBKj1JGcO1WuU701o8BCRIOwMGfoeNrUKcv/PyEnLFbrYdHvoKgenJdzazzcubSo7/L2Utjd8ntuQfCo99eU0B9Nussa6LWsDZ6LdE50QDoVDpCPUJxSA6UCiVNApuwYcAGzHYzldwqiYRjgiAIQkkpR+UkYYC0ZiLtflHycNt6vNG7TvnbkiRYM0EOqPU+8NAXNyWgBkgxpqBX63mw6oP8E/MPzQKbYbKZ2J20G5PdRLBbMNW8rpLnRLhrOCwWEt94k5xVq/AZOpTACa+V63zTyVMkTJyI5exZdDVr4lK3Di516mA+cYLsP1YVW9urDgrCtVkzXJs1xb1zZzSBgc59vkOG4PXQQ6TNn0/mTz9jS0oi+5cVZP+yApATgKm8vVF5e2M+dQrsdrSRkYR8MgeXGjXk92I2YzpyBIexAE1IMJpKlVDq9djz8ig4cADj3n2YDh9GGxmJ14D+uNQs3yxEhUpV5qnpznO0WrRhYWjDwsp1Xol2FAoUd0BAXR5ipFq4541aso/Vh5N4qFEwcwY1Lr4zKxZWDCsKiBs+BslHIOmw/FqhlKeyufrB48sgtGnJC+QmydPC934D5lx4cgVULv+a5hHrRrAtoWhakFappW+1vjzf4HkC3QKvcKYgCDeCuDdVLPF53mR7vioqZwX8aOtMZrcPGdXpGgLQbZ/AusmAAh7/GWr0qLh+loHRasTqsKJVaZ3lIN/c+iYrz67kydpPMqHFhJvaH6H8HEYjDrO53BmmL2bPySFu9Bhnki6A0C/m49GpU5nOz1rxK0lTpyKZL137X0QTHo5X/0fx7NULTUhImZYVOEwmjHv2kr91K3nbtmI5c7bEMZ4PPEClKe+gdHMrU1+Fm0eMVAsC8vQSs81xxSlt0x5uQBU/d/o3Cy2+I/UkfNNDzlCq85RLXtXtBw47/LdErqOZnwI+VeRA2adK6RfwCIJOE6H9K/I6M9211fvrEt6FXUm7aBvclh4RPegc1hl3rfvVTxQEQRCES8UXrgGt0hnObeAx9QYOak4B5Qyqj/5eGFADPafd9IAacGbzvsDusLM5bjMAncM63/T+CCVJFguW2Fi0VaqUCERtmZmcH/QYtrQ0qvyxEk3IFUqSXoY1IYGY557DcuYsSjc3XFu0IG/DBhInvYF+5e+o/f2dxzry8yk4ehSFRoPS1RWFRkv6l1+S/dtvALi1a0fA+JewREdTcPQo5uPHUfn44vXIw7i2bFnuck1KFxfc27fDvX07Aguvb8vMwp6ZgT0zE6W7O/rGjcW6/zucCKqFu5bN7mDUkv2sO55MnUqetKnqS+uqvgR6unA0IQezzcHgBh4YJEfJad/mXLkkSEEmVGoIAxYX1chUqqDJU3KAfeYfqNIR9GV4sqpSg6r0gDrTlMm3R74lKjvKuS3HksNjtR6jZ2RPAB6u/jA9InqIkleCIAjC9YvfB0B+4+GsPKXgcfW/1Ns3GVp2BXUZ11jG7obfnpd/b/E8tBp51VMkSXIGD+dzzrPyzErGNB5z5SRODhtqZcmvrAl5CQS7B5fYvvzUcjLNmejVepoEXn+iIuH6WJNTiH3+ecwnTuDz9FMETJzo/O8t2WzEvzQey/nzAGQsXkzg66+Xq31LTAznn3gSW2oq6sBAwhYuQBsRQfSAgZhPniRh4uuEfbkQhVJJzrp1JL/7HraUlJINKZX4jx2L73PPolAqcalTB8/777/u91/iMm5uaN3cILT8Dw+E25cIqoW7llqlpG6wgb+PJXM0IYejCTl8uaUoaK3tns/gLZPAlCOPQjccJO+QJPh9FKSdAo9geGIFuPuXvIDOA+o+dF19tDlsLDm+hAUHF5BrzS2xP8WYwn2V70OlVKFWqkVALQiCIFw/U448Gws4RjWm2wbRQ70f34zTsO1T6Pjq1dvIjoMfB4HNBDV6yaPUZfDx/o8psBYwuvFoHvvzMXItuVT3rk6vyF6XPefljS+jUWl4qelLhLjLgUh6QTr3/3o/Vbyq8EOvH5yj1TaHjfd3vQ9Afb/6pQbjws1jPn2amOeex5aYCEDGd4uRJInA119HoVCQMutDjDt3gloNNhtZy3/Bb/RoVB5FgxCS1UrK7I/RhITg8+QTxdqXJInEtyZjS01FV706YV8udGaxDvnoQ6IeeZT8bdtI/fRTLGfPkrtOTiqr8vFB6eqKw2RCKihA7e9P0JQpuLVscZM+GeFuI/6lEe5qL3arTvsafsRmGNl5Lp0dZ9PJNFqpHeTOe/lzISdVPvC35yH5KHR7B3Z8Bsf/kEtjDVhcekBdQRQo+PPcn+Rac6ntU5tHazxa7AtAfb/6Ilu3IAiCULES/wMkMISzL0NDDu78HjSaYUnvwZYPofkwcPW5chv//QjGdAisLyfpLMO9avW51Xx75FsAOod35qk6T/H5f5/z4Z4P6RDaATdNyfWkh1IP8W/svygVSkY1GoXJZmJD7AayzFnYJTs6pa7Y9G+1Us1nXT5j7oG5vNq8DA8HhBsmf+cu4saMwZGbizYiAsNDfUmd8wmZi78HwKVOHTK++w6AkI8+Iu2zuZhPnyFr2XJ8hz3jbCdj8WIyvpX/3qj9fPHs2dO5L/v3lRh37ULh4kLo/HnFykLpqlUjcOIEkqZMJf2LBfJGtRrfYcPwGzkCpYvLjf4IhHuICKqFu4rN7mDB5nMMaROBW2Gd6Sbh3jQJ96Zvo4um2exbBKt2gEoHjR6Hfd/C9k/lqWxxhQkues2AsMuU17oOuZZcXNQuaJQaVEoVr7d8nejsaB6s+qAIoAVBEITycTjKn2m7cOo3IU04HJ8NgKVmP5B+l5NxHv4FWj5XtjYaPQ66q+f3OJp2lMnb5bXXw+oNo01wG5oGNmXlmZXE5cWx4NACxjcdX+K8Tw98CkCfKn0Icw9j6NqhHEw9iLdOXnZV2gh3x7COdAzreNU+CRVPkiRMhw6R/ccqMpctA6sVfdOmhH4211l2KWny23JgXTgF3G/USDx7dMeRl0viG2+S8f33+Dw1GIVGgyUuntS5nznbT3zjTXQ1a6KLjMSWmUnKjBkA+I9+AW1oaIn+eA0aRN62beSt/weX+vWp9N675c6SLQhlIWrtCHc0u0PiUFwWX205x7OL99Ls/fXMWnuSZxfv5bKJ7TOjYe0b8u9d34I+c+CRr0HtArE75WzeDR+HZs+Ufv512J6wnX4r+/H14a+d2xoHNKZf9X4ioBZuCWtCAqmffU7yzFnY8/JvdXcqjCUunsxly0j64AMs0dG3ujuCcGOc2wTvB8oPistBcgbVTelYw58+DYNpUcUHGg+Wtx9YfJUGJEgoTHQWcuU1y0arkU/3f8rgNYMx2820C2nHmMZjALkk5MQWEwH4/uj3nMs+V+zcXYm72JW4C7VSzchGI9GoNLQObg1ApjkTBQpn3hHh1rLn5ZP6+eec7dGT6IGDyFyyBKxWPHr2JPybr51Zvb0HDCDo3anySZKEe5cu+I0eDcgZsFW+vtiSkshZ+zeSJJE0dQqSyYRr8+a4NmuGIz+f+HEv4TCZSJkxE3tWFrqaNfF5+ulS+6VQKAidM4eI5cuI+OlHEVALN4wYqRbuSFa7g7E/HmDbmTRyTLZi+zxc1AxpE1F60hOHHX4bCZY8CG8DrUbJ2+s/Kmfv/vU58KwED8x2PkG9XpIksTd5L8tOLuOv6L8AWBO1hmH1hqFRaSrkGoJwsexV/yNp6lQCJ03Cq99DJfZLNht5mzaRuWwZ+Zu3yF+QgbwNG4rVyCyNPScH4759SCYTCp0OhUaLQqdF7eOD2t8fpadnmTOYSpJE9q+/YT59Gr9RI1GVs4ySPTcX05Ej2NLSsGdmYc/OxpaSTP7u3VjPxziPy1n5B6HzPse1aSkl7wThTnb2H7BbYNMsOSC+zMPZqLR8vt56jjd71yEx24THyR34AYQ0ZUBEGAOaFdac9R8A696Sy0YmHpQTdZYmJwHykkGhgqAGl+3ewdSDvLLpFZLykwBoF9KOGR1mFHuI3DGsIx1DO7IpbhNTtk9hVsdZBLgGIEmSc5S6f43+zrXUoxqOIi43jv+d+x8tK7UkwDWgfJ+ZAMgZqDN/+hlrfDwe3bvj2rJFsX+7JUnCGheH0t39qmWurPHxxI4cJddbBhR6PR73dcPQ50Hc2rUtcU/w7t8ftY8Pxn378Rs10plNW6nT4f3E46R9OpeMb79FoVSQv3kLCo2GoClTULq5EfXww5hPniR2+LMY9+4FhYJKU95Bobn89ymFWo2+fv1r/agEoUxEUC3ckTQqJedS88kx2fBwUdMiwocWkT60DHOlnuIs6lCvkifZzLBxOsRsB40bPDSv+BeQkCYweo/8exmCAqvDyo6EHdTxrYOf3q/E/vSCdFadXcWK0yuIzol2bh9UcxAvNX1JBNTCDWHPzib5/fdx5OaSOHky2sqVcW1SVH/dkZ9P7PMj5C8jhVxbtsRy/jyWqCiiBwyk0pR3MPTtiyRJ2FJSsZw7i3HPHvK3bafg8GF5uullKHQ61AEBuNSqhUuD+ugbNERfr26J2psOk4mkt98me+UfAORt3kzY/HloK1e+bNu2tDTyt2/HuG8/BQcOYD592vlAoASVCn3DhjiMRswnThAzZCiVpk/D0Lt3WT5GQbgz5BXmBcmJg3MboVrXEofkmqw8u3gvZ1LykCTwsafzsiMNO0qMPnUoVpPC1QdqPQBHf4X930PvywTVF0apA2qD1rX0Y4Ag1yByzDkEuwXzWovX6BLWpdSHbhNaTGBn4k72p+zncNphuoZ3ZVPcJg6lHsJF5cJzDYqmoisUCqa2mUrbkLY0C2x2lQ9IuJTDbCbrp59IW/gl9vR0ADKXLkVbuTJeA/qjCQ8nf9s28rduk4NqDw9C5nyMe9u2pbZn3H+AuDFjsKeno/L3I/CVV/Do1u2q9ZY9unbFo2vJv6/egwaRvmAhpqNHSXzzLQB8n38eXZVIAEI+nEXMM8Oc9zDvxwahb9ToWj8OQagwCumyc2RvH2Utui3c3c6l5hHq7YpWLT/R3HI6FTedmgYhBtQqJZjz4Pt+8ppoFy9oMEB+cm8Ihb3fwK4Fcl1pgD6fQNMh19wXs93MSxteYkv8FtQKNZ3DO/No9UdpFdwKpULu32cHPmPBITkxhqvalV6RvRhQcwB1fOtcz8cgCFeUPG0aGd8tlh8MSRIqfz8if1mBJjAAR0GBHFDv3o3SzQ3vxx/D69FH0VaujC0zk4RXXiV/2zYAdNWrYU1IxJFfckq4NiICtZ8fDqsFyWJFMpmwZWTgyM4uvVMqFe7t2mF4qC/uXbpgS00jbuwYzMeOg0qFystL/kJmMBAy91PcWsjZVx0mE6bDh8nbto38LVsxHT1aomlNaCiasFBUBi9UXgZUXl7oGzTAtUULVO7uOAoKSHjtNWfGV/+XXpLLpVTATBRxb6pY4vO8Bj88Amfkv9vU7Qf9FxXb7XBIPPf9PtYfTybI04U/xrTFM/pvXFYM5rgjjDcrLWTmow2I9HVDqSz8f+Lsv/K91MUAL58Ejb7kdddPga2z5fKSD851brY77OxM3EnbkKIAbH/yfmr71kavLqWdixxMPcjKMyt5q9VbKBQKnv37WXYm7mRYvWGMazruWj4d4SKSJJHzxx+kfDwHW5I8c0ATHo5r06bkrl2Lw2i8/MkqFYFvTMLn8ceLtZe9ciVJb01GslrR1a5N2LzP0VSqdN19TXz7HbJ+/hkAbWQkkSt/R6nVOvenzptH2qdzUfv7U2X1n8UyhQtCRSvrvUkE1cIdISGrgAc/20YVPzcWDG6Kt5u2+AFWEyztD1GbS56sVIOjcIq4Zwi0HQctnr3m6d0FtgJe/PdFdiTuQKVQYZfszn2TW0+mf43+AMTmxjJh8wQerv4wvSJ7lZrVVBDKQpIkjDt3ogkOvuJIrvncOc492BdsNkLmfkrap59iPn0GfaNGhH25kLixYzHu2InS3Z3wb75G36D4tE3Jbidt3nzS5s0rGgFWKtGGheFSty5ubdvg1ro1muCSdWFBDoJtaWlY4+IxHT1KwaFDFBw+hC0h0XmM0sMDlEoc2dmofHwI+fhjtJERxI0eg+nQIVCrMfTpg/nsWUzHjoGt+PIOXZ3auLVshb5JY1wbNULtf/Xs/JLdTsrMWc4ss77PP0/AS+Ouet7ViHtTxRKf5zVY0EGepg2g0spB8EVZuz/6+yRz/z2DVq1k+fOtaRjmBf9MhS0f8bO9ExOs8gjwpPtr8VyHqvJJDgd80gCyY+Hhr6BB/5LXXdxXHhl/YA40GwpAXG4cr21+jcNph1lw3wLaBLe5rrdmtBr58cSPPFrjUVFO8jpZYmNJevsd8rdvB0AdFITfqJF49euHQqPBkZ9P9urVZK/4FXtuLm4tW+LWvh36Ro1ImT7dOaPI+/HH8ejRg9z168ldv95ZJsu9W1dCZs5E6Xr5WQvlYT4XxbnevUGSCF/8nfNB6wWSw0HO6jW41K6FrmrVCrmmIFxOWe9NYvq3cNsrsNh57vu9pOWZ8XPXOkeqnWwWWPaUHFBr3WHw72DOhgM/wIk/5fVmgfWhzRio9zBc57TrbfHb2JG4A71az+ddP8egM7Di1ApWnV3F5rjNzqA6zCOMpb2XXte1hLuX6eRJbElJuDZvftUvIlk//UTSlKkoXV0J/+479PXrlXpcyoyZYLPh3qkTnvfdh0uNGkT1H0DBf/9xtnsP7JmZKF1dCVu4sERADaBQqfAfMxqP7t2xxJxHFxmJJjy82AjBlShdXNCGhqINDcWtVUvndvO5KLJXriT7jz+cX8Jc6tYldO6nzgC98uLvSJw0iZzVa8j+7TfnuWp/f1ybN8OtfQfc27UtUxBd2vsKfH0imrAwUufOxfP++8vdhiDcli5M/9Z6gCUXDv0MrUYCsHxvLHP/PQPAtH715YAanFm73aq0BLlUNXWDLwpalUpo9ARsmi4nLCsMqpPzkxny1xA6hnZgQsIBFOBMUma0Ghnz7xjOZJ3BXeNOjjnnut+aq8aVYfWHXXc7d7OCw0eIf+klfIYMKVG/GeT8GRnfLSZ17lxnHgy/UaPwGfI0Sp3OeZzSzQ3v/v3x7l/yAUql6dPRVq1G6uzZZC5dSubSou81CldXfIc8jd/o0c510RVBVyWS0LmfItkdJQJqAIVSieEBsZRHuL2IkWrhlso2Wnnll4NYbA5e7FadJuHFk2EUWOx8892XfH/WFYtbJVa+0JYwn4sCEIcdfnkGjv0uZ+9+cgVEtCvab8yAvBTwr1lhiccAlh5fSi2fWjQJLMp6WmArICYnhpo+IrPkvUCyWMjbvBl9w4ZlDvTsefnkrP6TrGXLMR05AsgJXdw7dcSzVy/cO3QoUTfTuGcP54c+4xyxVXl7U3npEnSRkcWOy9uyldhnnwWNhip/rHTuz9uyhdjnngdJQqHXE/7lQlyb3Zp1iJLDgXH3bizR5zE81LfEe5UkiewVKzCfPo1LvfroGzdGExJcIVO1L7BnZ6MyVMyol7g3VSzxeZaTJMG7/uCwyjOwts2BgLowchvbzqbz9De7sTkkXuhclVd71JLPcThgRgSYszEP28SAlXmk5ppZN76jswwlAFkxMKcBIMGLB8E7guWnljN1h5y1eVhWNuNyzfB6HJJSzWubX+Ov6L/w1/uz5P4lVHK//inAwpVJdjtRj/bHfPw46sBAqm3cUOLfytRPPyVt3nxAzp1Raco7aCMirul6OevWkThhIgqNBvcuXfC47z7c2rQWtZ6Fu54YqRZueym5Jp76ejcnknIB2H8+k+2vd8HDRUNUWj4/7DxPyt6VzGU6j+vcie++uHhAbTHCr8/Cif+BUgMDlxQPqEGeBnfRVLiyWHd+HdW8qhFpKApa9ifvx0/vR7hnOACP1368xHl6tV4E1PeIvK3bSH7vPSzR0ai8vAieOQP3Dh1KPdZhNpO/bRu5a9eSu2590bo1jQa1nx+2xERy1/xF7pq/UPn4EDhxAp59+qBQKLDGxxP34jiw2fDo0QNrfDymI0eIGTaMiB9/RBMYKF/DYiF5xnQAfJ54oljA7d6+PUFvv03WsmUETJhwywJqkEcX3Fq1wq1Vq9L3KxR4PfroDe1DRQXU94J58+Yxa9YsEhMTqVu3LnPmzKF9+/aXPX7JkiXMnDmT06dPYzAY6NmzJx9++CG+vr43sdf3EFOWHFCDXMli53xIOQoJB9CqI3B3UTMwLIeXDRuhIAj0XpBxVp7JpdajC67LipEqFAoFKuUlD668wqFKJzi3AQ4sgS5vcDbrrHP3114GGrob6KzS8P3RxfwV/RdqhZqPOn0kAuqbJPPnnzEfPw6ALTkZ05GjxWYxSQ4HWb+sACDglZfxGTbsuh5Qet53H+7t26NQq1GoRfggCJcS/1cIt0RcppEnv9pFdLoRfw8d7av5UTXAHQ8XeWr2qoMJfL01im80f4EKvBV5eK9/Eny+h+rd5NHnHwfJ09hUWnj0G3n7dcowZfDm1jcx2830q96P5xs8z2+nf+OLQ19Q26c23/f6XmTtvodZ4+NJnj6D3HXr5A1KJfasLGKfex7fZ5/F/8WxKNRqbJmZ5G/dRt6GDeRt3FgsAYw2IgKvAQMwPNQXlbc3piNHyVmzhpw1a7AlJpLw2gSyfvuNwAkTSJg0CXtGBro6tQmePg1HQQHnH38CS3Q0scOHY3jkEfJ37MC4Zy+S0YjK2xu/USNL9Nt70EC8Bw28WR+TcBf4+eefGTduHPPmzaNt27YsWLCAXr16cezYMcLDw0scv3XrVp566ik+/vhj+vTpQ3x8PCNGjGD48OH8dtF0fqH88s02Fmw+R3xmAR8NkLNx55ltaLKS0AHoDOARCLX7wJFf4MD3NO/+PpsbbcTjwBcoztthy4fQbUrRjK1KDUGlufKXwCaDC4Pq76HDq5zJkqeS11UbqJ0RR/sqPdiTtIfZ+2YD8ErzV2gc0PhKLQrID0GNO3diS8/AnpmJPTMTla8P3gMHotQXT+YmSRL5W7ag9vPDpU5RklNbRgapcz4B5AeF9uxscv9ZXyyoLvjvP2wpKSjd3fF+6qkKmfEjRqUF4fJEUC3cdGdS8hj89S4Ss02EeutZMrwllX2LJ/Ea1DyMuOiTdI4tTMAS3hpidsCPA6HLm7D3W8g6D3pvGLQUKl97QhRJkrBLdtRKNSabiRZBLdgYt5FfTv3CL6d+cR5X1asqVodVBNX3qOxVq0ic/DZSQQGoVPg8+QS+zz1H2ufzyFy6lPQvvyR/505QKDAdPlys1JM6KAjPHt3x6NEDfePGxb7c6OvXQ1+/HgHjXiR90Xekff45xh07iXqoHwAqHx/CPvsMpV6PUq8n/OuviH7sccynz5AyfYazHZWfH5XenVruWs+CUJrZs2czbNgwhg8fDsCcOXNYu3Yt8+fPZ9q0aSWO37lzJxEREYwdOxaAyMhInn/+eWbOnHlT+323yTVZ6f3pVmIy5AdzQ9tGsPpwIt9si+KLdiY6AbgXLj9pMlgOqg//Amf/xTMzWt7u5g/5qbByFGgKZ3uFlKFme60HwD0IchPh8HLOZZ0DYJJJTf30TBQdmrE3aS92yU7vKr15vFbJGVz3IsnhwHT4MC5165YY0XWYTEQ//rhc+eASmUuWUmnqFNxatwbkvBtJ775Lwd59oFTiP24cvsOHoVAqSZk9G0dODrratfF5+ikSJ75O3j//EDBunLO9nDV/AeDRtUuZ82IIgnDtRFAt3FSSJPHqLwdJzDZRLcCdH4a1JMhQ8slngKcLM6scglgJIjvAEytg5QtweBmsf0c+yDsSnvgF/KpdV5++OPQFB5IPMLvTbILdg5nbdS77kvfx8b6POZh6EDeNG2+1eoveVURSjHuRZLGQPGMmmUuWAKBv1pSgyZNxqVEDgKDJb+HaojmJb7wpB9OFdLVq4d6+HR7duuFSv/5Vk7gotFr8nnsWz149SXpnilzaSq0m9JM5xbJta0JCCP/6KxImTETl7Y1bmza4tW2DrkaNCk0UI9y7LBYL+/btY+LEicW2d+/ene2F2YMv1aZNG9544w1Wr15Nr169SElJ4ZdffqG3qAt+XbadSScmw4iPm5b3HqpH3WBPNpxIwWR18N+J03JQ7SYH1Uk+LXAoAgk2J4M5R6520fsjqNoVds2HjTPAWlgiL6TJ5S5ZRK2Tk56tfxv7tjn0aP4IZ7LOUHXvn3KSsuAmjPSvQS2fWrQKblWhuQ/uZElTppL188+4d+pE6NxPUWiKHsQnvfce5mPHUXp4oG/YEJW3NyqDgdz167HGxhIz9BkMDz2E0sNDTghmt4NaDTYbqbNnY9y3F5/HHye7cFp30FtvoatahUS1GvPpM1iio9FGRCA5HOSuXQuAR8+et+RzEIR7jQiqhZtKoVDww7CWLNoezWMtwvG5tDTWBXYb7P9e/r3pEFBrod8CcA+AHZ9BaAt47Edw87vmvqQaU/nfuf8x7795AGyO28z9VeSswE0Dm/J9r+85kHKAUI9QAlwDrvk6wq1jPhdF/s4dGHr3vqa1tNbkZOJfHEfBf/8B4DdqJH4vvIBCpSp2nGfPnrjUrk32yj/QhATj1q49msBr+zujDQsj7Ksvyd+2HZWHO/qGDUsco6tenchfV1xT+4JwNWlpadjtdgIL1+xfEBgYSFJhfdtLtWnThiVLljBw4EBMJhM2m40HH3yQuXPnlno8gNlsxmw2O1/n5Fx/xui7zfl0OQhuV82P++vLa5X7NQnho3WnyEiJAw3OoHrxzhiSzf14X7cIl2aDoetkcCmcudL2RajfX64vnXEWqpVxuVSzobDlI1Rpp5hgaACVe8OO5aDzBF/5gXbn8M4V+p7vZMb9B5z1lfM2biRh0hsEz5iOQqkk67ff5WBYoSD000+cI9IA/uNeJPXjOWQuXUr27787t3t0707gxAnkbdtG8nvvk79pM/mb5NKhhocewrWJPN3erUVz8rfvIPeff/AdNoyCAwecU7/d2hbVDBcE4cYRQbVw07np1LzQ+Sqjy2fWQW4CuPrKU9BALvPR431oOQI8g0GpunIbpTiefpwfT/zI/pT9nM8579w+rN4wZ0B9gUKhKJbdW7hzOAoKSJs3n/RFi8BqJW3efILefBOPHt2doymS3U7+jp3YkpPR1aqJS/XqKLRaJLsd4+7dZP/vf+Su/RtHXh5KDw+CZ87Ao/PlvzxqK1fGf+yYCum/QqHAvZ34IiTcWpeOPEqSdNnRyGPHjjF27FgmT55Mjx49SExM5NVXX2XEiBF8/fXXpZ4zbdo0pkyZUuH9vptEFwbVEb5FSTpDvV1pVcUHv5hseYN7AAUWO0t3x5Dl6MB9D4+jZ/1SkoV5BsPDC4pt2hq/lfSCdB6s+mDp/21dDNDsGTmz+LZPoFHhFO9KDeV7suAk2WwkFf591jdpQsGhQ+SsWoXK0xOvgQOc+/xGv1AsoAZQubsT9NabeD7Qm+R330Oy2Qh47TXnfcC7f3/09esT/+I4LOfPo/TwIOCVl53nu3frJgfV6+Wgumjqd1cx9VsQbhIRVAs3zc5z6bSI8EF5aZbR0uxbJP9s9Lg8Be1iXmHXdP1sczbPrH2GPGseAAoU1PSpSY+IHjxT75lralO4uey5uaTM+hBteBhegx5D5V58Lb4kSeRt2EDye+9jTUgACpO4pKURP24c7l264DPkafI2bSLnj1XYUlOLTtZocKleHVtqarHtutq1Cf1kDtpSkjMJwt3Iz88PlUpVYlQ6JSWlxOj1BdOmTaNt27a8+uqrADRo0AA3Nzfat2/Pe++9R6VKJYO8119/nfHjxztf5+TkEBZ2bf++362i0+S11BF+xf+te6RJKNYYeWRfcvXj1wNxZBmthPu4cl/doDK1fTj1MK9teo3xzcZf+cBWI4nfswCP+F14lmf6+D0m4/sfMJ88icpgIPTzz8jftp2EV18lc8kSsleuRDKZcGvbFr+RJZNJXuDauPFlZyG51KpFxIpfyFyyFNfmzVH7Fc3U8+jSheSp71Lw339Yk1OKpn73ElO/BeFmEUG1cFPsO5/BoIU7aRTmxbLnW6NVX+EJd3YcnP5b/r3JkArrg0FnYHr76Sw6uoghdYfQOLAxnlqR1OlOkvrxHLKWLQMg/cuv8Bk6FO8nn8CWlETOajmDtiUqCgB1cCWC3ngDt/btSf9iAWlffknev/+S9++/zvZUBgO6GjUwnTyJIycH07FjACgNBjx79MDQ5wH0TZuKtcrCPUWr1dK0aVPWrVtHv379nNvXrVtH3759Sz3HaDSiviQpk6pwmYR0UdK+i+l0OnQ6Xan7BNmFkepLk3n2ql+JnavkoDrG4s43++V/94a0iShZHqsURquR17e+ToGtgFOZp668HtojiLcrV2eXLYvpKWfpDRAsguqLWZOSSCtc6hDw6iuovb0xPNAbe042yVPfxZGXhzowkOBZM6/rfqJyd8fv+edKbNcEBeFSvz6mw4dJnf0RttRUlB4euLe59iSugiCUjwiqhRvO4ZCYskoOVmoGehQF1OZciNoMZwuDnMiOclKyAz+A5ICI9techOxY+jEMOgMh7iHFtncM60jHsI7X/F6EimdNSQGrFU1IyBWPM58+TWbhWjVNcDDWhARS58whbd48JIvFeZxCq8Xn6afwGzkSpas8ZdJ/7Bg8e/UkccoUTIeP4NauHYaH+uLRsaM85VuSsMbFYTp6DKXeBbfWrVGIKXPCPWz8+PEMHjyYZs2a0bp1axYuXEhMTAwjRowA5FHm+Ph4Fi9eDECfPn149tlnmT9/vnP697hx42jRogXBFyXaE8rnowENiU4zUj3Qvdh2d52aanojmGDBvlzO5uTjrlPTv1lomdqdvW8253POE+AawAuNXgAgz5KHq8YVpaJk0HdWpQAbhFtt8oayZA+/jUl2O5LZ7LxHXK/kadNxGI3oGzXC8PDDzu0+jz8ONhvZv68k6O3JqH18KuR6pfHo2hXT4cNkr/zD+VrcxwTh5hFBtXDdTFY7+85nUjPIAz93edQh32wjNtNIYpaJHefSORSXjbtOzSs9asLR32HPVxCzExzWoob2fAUKJSgLM2U2HVLmPuRZ8vju2HcMrz+c/1L+Y8y/Ywh2C2bx/YtRK9QYbUb89Nee1EyoWI78fHLXryd75Uryd+wElYrASa/j/dhjpY6YSJJE8vQZYLfj3q0roXPmkLNmDWnz5ssj0xoN7m3a4Hl/L9y7dkXl7l6iDV316kT88EOp60IVCgXasDC0YuqpIAAwcOBA0tPTmTp1KomJidSrV4/Vq1dTuXJlABITE4mJiXEeP2TIEHJzc/nss894+eWX8fLyokuXLsyYMeNylxDKoE1VP9pULX1fJXUuAHkqbwAGNg/Dw+XqJR+3xG3h55PyA8p3276LQWdgT9Ie3tj6Bk/UfoKn6z5d7PhsczZp5kwAqlqtcmI0Q9mC99uRw2Ih5plnMB09RtXVf6IpZWlCWUkWC2kLv5SnW6tUBE15p8RItM9TT+Hz1FPX2+2r8ujWldQ5c5yvPcXUb0G4qURQLVyXMym5jF56gBNJuXz3TAs61pCzkC7aHs2stSeLHftC52r4n/oRVr1YtNE7AqrdBwoFnN0A6afBbgZXv6IEZVchSRJTdkzhr+i/OJlxkkktJ+Gh8eBs9lle3vgyEZ4R/Bn1J5NbT6ZnhLjJ3EoOs5m0uXPJWPojktFYtMNmI3nqu5iOHCXo7ckoL5kSmrdxI/nbtqHQaAh87TUUajWGPn3wvP9+TCdOoA0NLXN2b1H2RRDKZtSoUYwaNarUfYsWLSqxbcyYMYwZUzHJ+oSr05kzABjdpw2qwxJD2kRc9ZxMUyaTt08G4InaT9AmWJ4eHJsbS2J+Ip/s/4RWlVpR06em85wzWWcACHbxw1WbLWcRv4P/HU2ZMVOu/Qzk79iJ18P9rnJG6Qr++4/EtyZjPn0aAN9hw3CpWfMqZ9042qpV0UZEYImORunpWSIZmiAIN5YIqoVrIkkSy/fG8fYfRymw2tGoFAR4FAVCwV4ueLlqqGTQE2xwoXYlT5417IaV4+QDmj0DrUeD7yWP4LPj4PwOCKoHmpL1q0vz88mf+Sv6L9QKNc/Ue4YgtyA+6/oZT//1NDsTd7IzcScAnhqxfvpWMp8+Tfwrr2I+KT9s0VQOx9C3L4Y+fcj9+29SPppN9q+/Yj51itBPP3HWZpYsFlJmzATA5+mniiUMU6hU6OvWvflvRhAE4QbbHZVBdHo+TcK9qBbgUXynJd9Zc7pG1Sp8XMejlBZK+mDXB6QVpFHFUIVxTcY5t/er1o8NsRvYGLuRN7a+wbI+y5zTwM9mnQWgim8t6L/+mipv3C5y1qwhc8kS52vTsWNQSlAtORygUJT6ENZhNpPy4Udk/vADSBIqb28CJ03C84FbW5NdoVDg0aMH6QsW4NH9PjH1WxBuMhFUC+VWYLEz8ddDrPxPzq7cvrofHw1oSIBHYRBss/BQUAb9JrQGXeGN/uhv8MsoQILmz8L9s0p/0m0IhQb9r9qHuQfmklaQRq4ll42xGwF4qelLNApoBEBt39rM6jCLsRvG4pAc3B95P21CRMKOW0GSJDKXLCVl1iwksxmVjw+Vpk7BvWtX5xcW32HDcKldm/iXxmM6coQz3Xvg2rwZHl27Yc9IxxIdjcrXF9/C9ZyCIAh3u98OxPHj7ljGdqnG+O6XjIDmpcg/1S6gLbncpTQOyUElt0ro1XqmtZ+Gi7rowbVCoeCd1u/QO6k3JzNPsjdpLy0qtQCKgupqXtXu6IDaHBVF4ptvAaCrWRPzyZPO5JQXkywWoh55BMnuIHj6NPQNGjj32VJTiR09GtPBQwAY+vYlYOIE1N7eN+dNXIXfyBFow0Lx6NHjVndFEO45IqgWym3KqqOs/C8BlVLB+PtqMLJj1aIyWeZcWNIfRcwO+bUhHPxrwLmNcvKxxoOh18zrnjq26uwqEvMTna+7hHVhcJ3BxY7pGNaRD9p9wKbYTUxsMfG6rieUnTU5hdz16zCfOo35tPzHkSuv/XPr0J7gDz4oVgrkArc2bYhYsYKE116jYP9+jDt2Ytyx07nff9yLpa6VFgRBuBtdrpwWAPlp8k+3gDLfT5UKJeObjWdY/WEYdCWXy/jqfekZ0ZMVp1fw+5nfSwTVVb0us7j7DuAwmYh/cRyO/Hxcmzcn8M03ierbF9OJE0h2OwpV0cOCgiNHMJ+Wp7xHP/4E/mPH4jvsGcynTxM7chS2xESUBgMhs2bi3qHDrXpLpVK6uOD16KO3uhuCcE8SQbVQgsXmYNSSfVQL8GBCz5rFpj8disvipz2xAHwzpLlzDTUA5jxYMgBidsgJxyQHZMfIf0Beh9XnE7iGchJ7kvbQKKARmsIkZk/UfgKz3Yy7xh1/V386hXUqdZpW7yq96V3l1k7JuleYTp0i49tFZP/vf2C1FtuncHUlYPx4vJ94/IprmrWhIUQsXYLl/Hly//mX3H/+oWD/fvSNG+N1UUZVQRCEu935y5TTAiC/cKTa3b/kvqsoLaC+4KFqD7Hi9ArWnV/HpJaTcNe606dqHyp7Vqa+X/1yX+t2YNy/n5RZH2I+dQqVry/BH32I2tcXhV6PZDRiiY5GV7XogUHB/v0AKF1dcRiNpM6eTd4//2A6fRrJaEQbEUHo/HnoIiNv1VsSBOE2JIJqoQStWsmDjUKY9OthVEp4tUct5776IQZmPtqA6LT8kgH10gEQsx10BnjqN/COhNQTkHIMJAmaDi331LFcSy7fHvmWLw9/yZO1n2RCiwkAJbKTCjeOo6CAzCVL0NWqjXu7tiX2W6KjSXr/A/K3bHFu0zdujGuLFuiqVUNXozrayEiU5Vjfpa1cGd9nhuL7zFDsefkoddpiIwmCIAh3M5PVTkK2CYDI0kaqL0z/drt6UG22m3lr61sMrjOY+v5XDowb+jck0hBJVHYUW+O30jOyJ32r9aVvtdLrk18Pa0oKjuxsdNWrV3jbAAVHjpL6ySfOe5NCpyPko4/QBAQA4FKrFgUHDmA6dqxYUG3cJwfVfqNHozIYSHr/fQoOHgTAtXUrQufMKXNiTEEQ7h0iqBZKZbLayTPb+HzDWYK99DzRIhxsZhTmXAZEWiC4QC6JZTPLf7Z9Aue3gc4TBv9WVMOychv5TznYHDa2J2xn1dlV/BvzLxaHXINYQiq1HJJwfayJiaR+8imSxYzfmDHFnr5bYmKIGzPWmVzM99nh+L/4Igq1/E9H7oYNJLw2QZ7erVTicd99+D4zFH3DhhXWP5V7KV8oBUEQ7hCf/XuaRdvP06teEEPaRlDV/+rLWGIy5KnfHi5qvF1LKZPlnP599aB6yfElrIlew4HUA6x+eLVzxldpFAoFr7d4HYPOQG2f2ldt+3rEPvsc5nPnqPLbr+iqVauwdgv++4+0BQvJ27BB3qBS4fXww/iNGlmsfJZLnTpyUH30GIY+fQA5QVnBgQMAuDZtgr5hQ1ybNiF55ix0VaviP3YMCs3Vy5YJgnDvuaaget68ecyaNYvExETq1q3LnDlzaN++/WWPX7JkCTNnzuT06dMYDAZ69uzJhx9+iK+v7zV3XKh4ZpudU0l51A81MKBZGAlZBcxZf5rvVv5Fv/XTcbVmXLkBrYccUIc2veY+bIvfxpQdU4qtl65qqMpzDZ7j/ir3X3O7QkkOi4WMbxeR9sUXSAUFAOSsW4/PU4PxGzkS4549zoBZ6eaGIz+f9C+/ouDwEUI+nEXmzz+TNvczAPRNmhA87QO0hTVsBUEQBNlvB+JJyzPz/c7zfL/zPJ1r+vNy95rUCylltNNmgaX9CUmJ5lNNJdJca6OIdoWwlqC+aLaPc/p3wBWvnWHK4MtDXwIwpvGYKwbUF7QOLirFFJUdhdFqJNIQiavG9epvtoxsqanOh7VZv/1G4KuvXneb+Tt3kvbFAow7C3NxKBR49nkA/xdeKPXe5FJYOeLiZGWWqCjsWVkoXFxwqS0/VNBGRBA27/Pr7p8gCHe3cgfVP//8M+PGjWPevHm0bduWBQsW0KtXL44dO0b4RaVuLti6dStPPfUUH3/8MX369CE+Pp4RI0YwfPhwfvvttwp5E0LFWLjpHLPXn2JMl+qMv68GL3atTkJWAa0Pfu4MqCUUKHQecrZRjYuceVSlBVdf6PzGdQXUAP/G/EtifiLeOm/ur3I/far2oY5PHTE6XcHyd+wg6Z0pWM6fB0DftClKV1fyt2wh4+tvyP5lBfbsbHlfo0aEfDKHgv37SXjjTYw7d3KmazcksxkA78cfJ3DiBFG+QxAE4RKSJBGfJT+0bFXFh11RGWw4mcrITpcZmY3bDec24gY8qIoG4w747huo3gOeWFZ0XBmmfxfYCnhn+zvkWfOo7VObB6o8UO7+Lzm+hJ9P/szQukMZ32x8uc+/bN8OH3b+nrPqfwSMH3/NS3wkSSLlww/J+PobeYNajeHBB/F9dvgV1z271K0DyEG15HCgUCoxFq6n1tevL+5pgiCUS7mD6tmzZzNs2DCGDx8OwJw5c1i7di3z589n2rRpJY7fuXMnERERjB07FoDIyEief/55Zs6ceZ1dFypSdFo+czecQZKgWoA8NU2hUPB+V18UR+Snvs+o3mf2+OF4uZWtfnRZFdgK0Kv1ALzc7GV89D4MrTu0Qp+KCzLJZiN17mekL1gAgMrfj8BXX8WzTx8UCgW5GzeSMm26M9j2fuIJAie8hkKrRdOrF7rq1YkbMxZLVBQKrZagd97Bq5Qan4IgCAKk51swWR0oFLD4mZYkZBWQumoyzbYshJCloL1keUvsLgCsoa2I922Ld+YhDDHr4NwGeRT7wmj1VaZ/x+XGMW7DOE5mnkStUDOhxQRn3emyyDRlMnvfbH4/8ztQ8Zm/CwpLUgHYUlIw7tqFW5trK3uZNvczZ0Dt/fhj+A4fjiY4+Krn6apUQaHV4sjLwxoXhzY8nILC9dT6pk2uqS+CINy7yhVUWywW9u3bx8SJxcsTde/ene3bt5d6Tps2bXjjjTdYvXo1vXr1IiUlhV9++YXevUVG5tuF3SHx1sojWGwO2lf3o0+DojVHmn1fAXYSvZow8bGnKzSgPpV5irkH5pJhyuCHXj+gUChw1bjyQqMXKuwaQhFrcgoJL7+Mce9eALwGDCDgtVeLlany6NQJtzZtyP79d9T+/nh07lysDV21akQsX0b2ihW4tmqFS81LaqcKgiAIThqVkikP1iXTaEGrVhLh60pEwo9gzYfT60gK7clHf5/k7Qfr4q5TQ+xu+by6fYloPUpO8jkjAkxZctLP4EZyw1eY/p2cn8ygPweRbc7Gx8WHjzp+RNPA8s0ic9e4syl2k/N1Na+KW/MMYDosB9UqLy/sWVlkr/zjmoLqtIVfkjZvHgCBkybh89Tgq5xRRKHRoKtZE9Phw5iOHUMbHo7xgBxUuzYRQbUgCOVTrqA6LS0Nu91OYGBgse2BgYEkJSWVek6bNm1YsmQJAwcOxGQyYbPZePDBB5k7d+5lr2M2mzEXTi0FyMnJKU83hXIw2+y89PN/bDmdhlatZGrfekVTrS35sPdbACr1eJlKgR4Vcs1z2edYcHABa6LWICGhVCg5mXmSWj61rn6ycFmSw4Hl/HlMhw5RcOgwluhoFFotSr0LCr2evA0bsWdkoHR1JejdqRgu82BLqdXiPWDAZa+jcnfH52mRfV0QBOFqDHoNT7eJKNqQlyIH1IAUtZlRm/zZH5PFqeRcfhnRCk3hSDXhLeWfCgUEN5ZHqhP2FwXVV5j+HegWSLfwbpzMOMnHnT8myC2o3P3WqDS0CWnDn+f+BCDSUHHloySHg4LDRwDwGzuG5KnvkrNuHUFvT0bpWrYZapLFQsaSpaTOng2A/8vjyxVQX+BSp44cVB89imuzZljPx4BCgb5Ro3K3JQjCve2aEpVdur71ShmZjx07xtixY5k8eTI9evQgMTGRV199lREjRvD111+Xes60adOYMmXKtXRNKId8s43nv9/H1jNpaFVKPhnYqHjpjv+Wyk/HvSOhZq/rulaOJYe/ov5i5dmVHEotmvbVI6IHLzR6oUJv2HcjSZLI27gR0/Hj2NMzsGWkY8/MwpGfj6PAiFRgwp6djSMv74rt6GrVInTOx2gjIm5OxwVBEIQiGeecvyqiNjP5ock8/c1uDsZls2HbdroXZCKp9Xxy2IWwpDgeahyC6kJQHb8fmj0jTwM3ZcmNuJWeqGxSy0lISOhUumvu6tC6Q/nz3J/U9a1bocuxLNHROHJzUbi44N2/PxmLvsMaE0PuP/84s3BfymGxkDJ9Oqajx7AmJmJLTZVH8QG/UaPwe/bZa+qLc1310WPO9dS66tVReXpeU3uCINy7yhVU+/n5oVKpSoxKp6SklBi9vmDatGm0bduWVwszOzZo0AA3Nzfat2/Pe++9R6WLyhtc8PrrrzN+fFFCjJycHMLCwsrTVaEM9kRnsO1sGq5aFQsHN6Nddb+inQ4H7Jwv/95qZLnrS1/qePpx3t35LgAqhYr2oe0Z1XAUtX1vbMmOu0Xm9z+Q/MEHVz1OodPhUqcO+gYN0NWogeSwIxUU4DAWoPT0wOvhh1G6VOyaeEEQBKF0h+OysTocVPV3x6DXFAuqST9NI4OR5zpUYdbakxza8TfdgQL/BszZGI2HTs3DTUIgpHAqcsJ/8k9j4XpqhQr03s7mLh7g0KquP8lWTZ+a/PHQH3hqKzbALDgkP1h3qVsXhUaDoU8f0j7/nOyVf1w2qM765Rcyl/5YbJtCr8d3+DD8Ro265r641CnKAO5cT92k8TW3JwjCvatcQbVWq6Vp06asW7eOfv2KkhOtW7eOvn37lnqO0WhErS5+GVVhhkep8CnjpXQ6HTrdtT9dFS7P7pBQKeWbbqeaAcx4uAE1gjxoFOZV/MDTayHjLOgM0OiJcl8nKjuK8znn6RTWCYDmQc1pH9KelpVa0rtKb/z0flduQHCyREeTUjjFzeO+bmirVEXt443KxwelmztKVz1KvR6luzva8HBRQ1MQBOE28fH6U/x7IoUP+tXn8ZbhxYNqgKjNDG79CF9sPEtY3mFQQ5KhEQCV/VzlIDm4MKhOOQYW40VTv/1AWZR8bHPcZmbsmcGDVR9kRMMRFdL/8swiy1m9muSZs6j07lTcr1Bm1VQYVOvr1wfA8KAcVOdv3441JQVNQPHRd8luJ+O77wDwGToUz/vvRxMSjMrb+7org+hqVAe1GntWFjlr1wLg2vT6qpgIgnBvKvf07/HjxzN48GCaNWtG69atWbhwITExMYwYIf8D/vrrrxMfH8/ixYsB6NOnD88++yzz5893Tv8eN24cLVq0ILgM2RmFimF3SPxxMJ5P/znDh/0b0rSy/HR7QPMwMGXDD4/KiU/8a0NALTi+Sj6x6dOgc79CyyV9cfAL5h+cj6fWk7WPrMVV44pSoWRet3kV/bbuepLdTsKkN5BMJlxbtyLk009FeTFBEIQ7RHymXE4r1FuucOEMqnWeYM6BqM14NhzE4NaVabr9FACntPIMrgjfwuVYnsHyNO/8FEg+AqbCPDOXTP3enrCd2NxYUowpN/ZNlcJ85ozzXpW1bPkVg+qCQ3I5LX0DOajWVq6MvlEjCv77j5w/V+M7dEix4/M2bMB6PgalwYD/mNFlXnddFkqtFl316piPH8dWOAtT31gkKRMEofzKHVQPHDiQ9PR0pk6dSmJiIvXq1WP16tVUrlwZgMTERGJiYpzHDxkyhNzcXD777DNefvllvLy86NKlCzNmzKi4dyFckdXuYOi3e9h6Rp4y9tWWczStXPgk1m6FZU/L67UAEg8WnahQQcvny3Wt7499z+f/fQ5Ao4BG5FhyRGmsMnCYTMSNGYtkMhHw8nhnkpSMxd9TsH8/SldXgt97TwTUgiAIdwhJkojLNAIQcmlQ3WAA7PkKojaDJDGsqQHfnQkAzD/rC1CU40ShkKeAn/pLXlftUjgd2714krLtCXIVlrbBbW/guyrJYTIR/9J4JJMJAOO+fZfNteMwmzGdPAmAS4OGzu2Gvg9S8N9/ZP/+Oz5Dni52bvq3iwDwHjiwQgPqC1zq1MZ8/DgA6sBANCFiwEcQhPK7pkRlo0aNYtRl1rAsWrSoxLYxY8YwZsyYa7mUUAGmrT7B1jPy2ukXOlcrykQqSbBqnBxQa9yg13TIS4aU45B2Gur0BUNoma+z6uwqZu6R64+PbTyWZxtcW+KQe1Hqp3PJ37IFgOhBj2F4+GG8Hu5H6pw5AARMnIAmJOQW9lAQBEEoj+wCK/kWOwAhXnr5npsRJe9s+Djs+w6yYyEzCt8MeZQ60zUSXH0hPYvKvhclDg0uDKoTDkBAYS6Si0aq4/Piic6JRqVQ0aJSi5vy/i5I/mAa5tOnUfn54cjNxZ6RgSUqCl2VKiWONR8/DlYrKh+fYsGrZ69eJE+bjvnkSdIXLMRvhPxAv+DQIQr27QONBu8nyr8UrSxc6tYle8WvgLyeWjy8FgThWiivfohwJ/vjYALfbJNv4nMGNuKFztXkWpgAmz+E/34AhRL6fwtNnoIOr8Kj38CILdDhlTJfZ3PcZiZvmwzAk7WfZHj94RX+Xu40uf/+S+yIkeRu3HjZ/AEAxgMHyCh8GOVWOGUu+9dfOf/kYCSzGbe2bfHq3/9mdFkQBEGoIHGFU7/93HW4aFRQkAnmbHlnYB0IbS7/fm4TFJbS8q7ZlrRcuaRopN9Fo7LBhcmzEvZDfqr8u1tRbpILo9QN/Bvgoa2Y8pdlkbNmDVnLloFCQcjMGegbNADAuGdvqccXTf1uUCx4VXl5EThpEgCpc+aQ8/ffAM57o+H++9EElp7p/Hrp69Rx/u7aRKynFgTh2oig+i52MimXCb/ICUFGdapK97oX1ao8+DNseE/+/f5ZUKPHtV8n4yQvb3wZm2Sjd5XevNr81XviSa8kSRj370eyWErss2VkkDBhInkbNxI3YiSxw5/FdOpUieMcJhOJk94AhwND376Ef7mQykuXoqsjj0Qo3dyo9O7Ue+LzFARBuJvEXW49tWcIaPRQpaP8OmqzM6i2BrcgIVs+r/hIdWFQnXa6qB33oiBzR8IOAFoHt74B76R01vh4Et+SH6b7Pvccbm3a4Nq8GQDGfZcLqgszfxeup76Y96CBeA+Wa00nTJhI7vr15KyVg2ufS9ZZVyRdzZpQmEBXZP4WBOFaXdP0b+HO8P3OaAqsdtpV8+Pl7jWLdsTvhz9Gy7+3GQvNr29UOdIQSavgVtgcNt5t+y5Kxb3xrCb9y69InT0b965dCf1sbrHAN/WTT3Hk5qIOCMCemUn+tm1EPdQPr0cewfvJJ3GpWQOAtM8+wxIVhdrfn8BJrwPg2qQxkcuXk7dxI5qwMDQioZ8gCMId57LrqX0Kp0VHdoCN0yBqk5zVG1BVbsWmV8KITs/H1+2islju/mAIk6eLn9skbyuc/m1z2NiZsBO4ueups1aswJGXh75hQ/zHyN8pXJsVBtV7LxNUH76Q+btBqfsDJ7yGJSqK/K1biRstLxt0bd0Kl1q1Krr7Tkq9nqC33sSWkoLLRaPWgiAI5SGC6rvYlAfrEebtSv9mYc4yWhgz5MRkdgvUvB+6Tbnu62hVWmZ3nI1NsqFR3hvlnOzZ2aR/+SUAef/8Q+b33+Pz1FMAmI4fl6fDASGzP0IdGEjKrA/J/ftvspYvJ2v5clzq1cO9Y0fSv/kWgKApU1AZDM72FSoVHl273uR3JQiCIFSUdtX9eKdPHcJ8CqdxO4PqwjJVIc1ArQdjuvxa743SrzrhSiXhvqUk5ApuLAfV1nz5tZucqMxoM3J/lfs5nHaYur51K/Q9WJOSULq7o3IvWQUkf9duAAyPPoKisHSqvlEjUKuxJSRijY8vlgvEnpWF9bycyFZfv16p11Oo1YR8PJvoQY9hOXsWAN+hQyvyLZXKe9CgG34NQRDubvfGkOI9Ij3PzNsrj5CeJ6/HUikVPN+xKj4XnnY77PDrs5AdA96R8ND8YjUuy+NkxknmHpjrXCusUWnQq/UV8j7uBOnffIsjNxdl4ReN5FkfUnD4CJIkkfzBNJAkPO/vhWuzZmjDwgj99BMq//A9Ht27g0aD6cgR0j7/HBwOPB/sg0eXzrf4HQmCIAgVqVaQJ0PaRtK1dqC84dKRarUWKl80XTus5ZXvySGXlHoqzP7tqfXkzVZv8vMDP6NSqiqo95D7zz+c6XYfMU8PKbHPUVDgnMrt1rKlc7vS1dU52nvpaHXBYXk9tbZyZVReXpe9rsrDg7D581BXqoRrs2bOXCOCIAi3MzFSfRcosNj5Yed55v57mhyTDbPNwfRHSplatXkWnFkvPxkf+D3ovcrU/unM08zYPYMMcwZapRatSsvZrLPkWHLw1HrydN2nK/YN3UbM56JQ+/uh8ihK/GJLTyfj++8BCJ4xnezfV5K7bh3x48fj9/xzGPfsQaHTEfBK8URvrs2a4dqsGbaMDHJWrSLr95UolEqCCpOzCIIgCHexC0G1d2TRtsgOcPZf+fewq2TtDr5kva/bjUncBZC/cxfxL40Hmw3T0aOYz0Whq1LU74IDB8BqRR0UhCYsrNi5rs2aYTp0COPefRj69i0658J66oalT/2+mDY8nGrr14FSKXKKCIJwRxBB9R3MZLXz4+4Y5m08S2phttA6lTx5tOklZbAcdjj6G2ycLr9+4GMIKpkkpDQJeQkMXTuU7AsZSy9S17cu/ar3u673cDvL+GEJye+9h8rfj/CFC3GpLScPS1/4JZLRiEuDBrh36YJr8+aYjh3DGhtL4ptvAeA7fPhl10KrfXzwefppfJ6+ex9GCIIg3OtWH06kksGFeiEGNCplyZFqgMiORb+HtcRoNTLoz0EEuwfzRbcvijdYqVHx125+5FnyOJV5igb+DVArK+YrXcHhI8SNGiUn4VSpwG4nb8OGYkH1hanfbi1blAh6XZs1I+Obb0qMVBt37wEuv576UgpVxY26C4Ig3GgiqL5DHU/MYei3e0jKMQFydtGxXarzSNNQef20ww7RW+DYSji+qqgER9Oh0OixMl8nyC2IbuHdOJlxktGNR2Nz2LA4LCgVStoEt7lrp3xnLP6e5A8+AMCemsb5JwcT+tlctJGRZP74IwD+L45FoVCg8vQkZPZHRD/xJNhsqIOC8B0+7FZ2XxAEQbhJjsRn88Hq47zeqzb1Q+XcGLkmK6OW7Afg6JQeaKy5RWunfS4aqa7UEPxrg9UIIU1JNiYRlR1FVHYUaQVp+OmLymah9wKfqpBxFvTeoNLw19mVTNkxhfp+9Vnae+l1vxfz2bPEPvssDqMR11atcG/fnpRZs8jd8C++w55xHmfcJWcrd23RskQbrk2bgEKBJSoKW1oaaj8/cv/9Vz5Hrca9fbvr7qcgCMLtRgTVd6i5/54mKceEt6uGV3rUpH/TMLTqwrVYkgQrhsmj0xe4eEH9/tD9vVLbO5d1joT8BNw17rhp3PB28cZP74dSoeStVm9htptx1ZSSOOUulPHddyRPk0f1fYYOxXT0KMbdu4l57nn0deogWSzyOq82bZzn6Bs2JOjNN0j5eA5Bb09Gqb87HzYIgiAIxQ1dtIfUXDMjftjHtoldAIjPkstiebtqcNOpISFKPtgtAHQX1ZFWquC5jYAEGj2RhkiqGqpyNvssR9KO0CmsU/GLhTSBjLNIbv4sPb6EmXtmAtCqUqvrfh+O/Hxinn0We1YWLvXrE/rZZ9izskiZNYuC/QewZWai9vbGkZ9PwZEjALi2LBlUqwwGdNWrYz51CuO+/bi3a0vSu/J3D9+hQ9BGRFx3XwVBEG43Iqi+Q816tCEda/jTLMKHqv6XZOU8+JMcUCs10OhxqNNXXrelKj0z97nsczy66lGsDqtzW7uQdsztMhe1Uo1KqcJVefcH1JIkkfHNt6TMmgWA74jn8X/xRSSrlYQJE8hd8xcFBw8C4D/uxRJT3rwHDRIZRAVBEO4hmfkW5/Kr+KwCjBYbrlo1cRlyUH3ZcloX07gUe1nPrx5ns89yOO1wyaA6uAnWw8uZ7q5m2W754e9D1R5iRMMR1/1eMpYsxZaQiCYkhLCFC1C5u6Fyd0NXowbmU6fI37IFw4MPYty/H2w2NCEhaENDSm3LtVkzOajeu5eC/fuxJSaiCQ3Fb9So6+6nIAjC7Uhk/75DuenUDGweXjKgzkmANRPk3ztPggc/hWpdLxtQA3y450OsDit+ej/CPMLw1nmzJ2kPf5z94wa+g9uLNTGR2BEjigLqkSPwf1EOnJVaLSEffYT3U4MBcO/W1VmLUxAEQbh3nc8wEmwoCoo3n0oDikaqQ7zKEFQXyjZnk5iXSD0/udzU0bSjJY4x1e3LyCq1WabIQ4GCl5u+zNQ2U9GqtCWOLQ97Xh4ZX38NgP/YMai9vZ373AurU+Ru2ABcNPW7lFHqC1yby/fInL/WOBN7Br39tpjFJQjCXUuMVN9hLpSwKjUbpiTBH2PBnA0hTaHN2Ku2ty1+G1vit6BWqPm2x7dEGCIquMe3N8nhIGvZclJmzcKRn49Co8F/3Dh8nhla7DO+kKXb58kn0VSqdAt7LAiCINwuGoV5sXVCF178+T9WHUzg72NJ9KwXRFymEYBQ7ws1qgunf18hqF4dtZoPdn1AVUNVAI6ky2UaL74XrUzYyi4pH1e1KzM6zCg5kn2NMr77Dnt2NtoqVfB84IFi+zw6dyb9iwXkb96CZLEUS1J2OfqmTQE5JwmAZ+/eYi21IAh3NTFSfYdZfzyFnnO2sHxvbMmdB36AM+tApYO+80B19WcmycZk9Go9g2oNuucCantODrHPPkfSO+/gyM9H37Ahkb/9iu+wZy5bwkMbHo5Cc/lRf0EQBOHeolQqeLJlOG5aFTq1CjKiqHPuG9woKGWkOvKy7WxP2A5Aj8geaJQass3ZxOXGFTvGz9WPVpVaMarRqMsG1A6LpVz9t2dnk7HoOwD8R79QIuu2S/36qPz8cOTnk7txI6aj8gi6a4vLB9WagAA0lcMBUHp6Evj6xHL1SRAE4U4jRqrvMD/ujuFkci6nU/KK78iOg7WF9Y67vAEBtcrU3sPVH6ZdSDtc1C5XP/guYk1IIPb55zGfPoPCxYWAl8bh/eSTooSHIAiCUCZxmUYCPV3QqJQ0i/Bh31v34aJRwU9P0C/9fzg0J3D3Lhydvcr0b6vdyu5EeQS4Y2hHjqcfR6VQFct1AtA1vCtdw7tetk8ZixeTPG06IR9/jGfPHmV6H+nffosjNxddjRp49OxZYr9CqcS9U0eyf1lB6sdzwOFAEx5+1Vlbnt17kP7llwS+/jpqP78rHisIgnCnE0H1HSQ+q4CNJ1MAeKxFeNEOSz4sewrMORDaHFqPLle7Aa4BFdnN257pxAlin3seW0oKan9/whYucNagFgRBEISyeGHJfmIyjMx7oimtq/qiUqrA4YDz2wB4RLWFNOkoWDwgL0k+6TIj1QdTD2K0GfFx8aGWTy0+7fJpufvjKCggbd58kCRSP/kEj+73oVBeeUKiLTOTzMXymme/MaMve7xH585k/7ICS5Q8jf1KU78v8B/3It5PPokm8N76jiEIwr1JTP++g/y8OwaHBG2q+hLp5yZvtNtg+VCI3yfXrey3QC7RcRVLjy9lT9KeG9zj20/epk2cf+JJbCkp6KpXI+Lnn0RALQiCIJRLdFo+B+OyyTHZqB54UcLQtFNQkOl86bdpkrwN5Hu03pvSXJj63apSK5SK0r+ancg4QVpB2mX7lL3yD+xZWQBYoqLI27jpiu/Blp5Oykcf4TAacalTB49u3S57rFvr1ih0Oufr0upTX0qhUomAWhCEe4YIqu8QNruDnwvXUTtHqSUJ/nwJTq8FtQs89jP4Vr1qW7+e/pXpu6fzzNpnOJpeMrvo3ciakkL8y68Q+/wIHPn5uLZoQeUlS9AEB9/qrgmCIAh3mD8OJgDyQ24/dznYlCSJz75bDECBX31w9YPUE7CmcD3xFZKUXQiq24a0dW6TJIm43DhsDhsA72x/h87LOvPP+X9KnC85HGQsWgSAJkQuc5X+zdcljrPExZPy8RyiHnmU023bkf3LCgD8xo65bC4RAKWrK26timphu5ZhpFoQBOFeIoLqO8Sv++NJzjHj66alR90geePG6bB/MSiU8Og3EH71J8fLTi7j7e1vIyHxeK3Hqetb9wb3/NaSbDYyFn/Puft7k/Pnn6BU4j14MGFffYnK0/NWd08QBEG4w0iS5AyqH2xY9GBWoVDQTHESgGXZdUhoWZjnJHan/PMyQXWmKZNj6ccAaF2ptfMavX/rTa9fe3E262yxYxr4NyjRRt7GTViio1F6eBD25Zeg0VCwdx8FBw86j7EmJRE9aBDpCxY4k43patUi8I03cO/Y8arv271LFwC0kZFoAsQItCAIwsXEmuo7QJ7ZxgdrjgMwtG0EWrUSdn8Jm6bLB/T+CGr1vmo7S44vYfpu+Zwnaz/Ja81fu2F9vlnk8h670FWtWmzU2WGxkPPHH6R/9TWW6GgAXBo0IOjtyejr3t0PEgRBEIQb53hiLmdS8tCqlfSoF1RsX327HPiuy6/ClqgGfFW5rXON9eWCahe1Cx92/JDTWafxd/UH5AC9klslYnNjOZJ2BFeNKxIS1b2rO4+52IVRaq8B/dFVicTwwANk//Yb6V9/Q+inn+AwmYgbPQZ7WhraqlXxfXY47m3bovYv2dblGPo9hOX8+TIF4IIgCPcaEVTfAdx1ar5/piVLd59nZKdqsGshrHlV3tnhNWj2zFXbWHp8qTOgHlpvKC81eemKU73uBJLFQuyYMeRv2gzIT8/d2rRB7edL5o8/YUuRk7opDQYCXnoJr/6PiuzegiAIwnX53yF5lLpzTX88XS4qsZgdh1tBAjZJyQFHNVpIkvzQ+4t24LCBd+lJyvRqPd0jutOd7sW21/Orx+6k3RxJP4LVLmcBbxvctsT5BUePYty9G9RqfJ58EgDfZ4aS/dtv5K5bh+X8eVI//xzTkSOovLwIW/AF2tDQcr9vpVZL4Guvlvs8QRCEe4EIqm9ndps8tVuppH6ogWmhDWDnF/DXBHl/23HQedJVm0nOT+ajvR8B8Gz9ZxnT+Mprp2439pwclG5uxQJiyW4nYeJEOaDWaMBuxxIV5cxMCqAODMRnyBC8+vdH5e52K7ouCIIg3EUkSeLPw4kAPNDgkpwcMfI072NSZfLRU6uSp1ze8v4P4ehvULNXua5Vz68eAEfSjpBRkAFAm+A2JY7L+HYRAJ49ezrLXOmqV8etQ3vyN28h5rnnsJ6PAZWKkDkfX1NALQiCIFyZCKpvU1LKCSxfdEal1qCObAuV28ilszZOkw9o9xJ0fRvKEBy7qF14ss6TnMw8eUcF1A6zmbTP55H+9ddogoLwHfE8Xn37gkZD0jtTyFm9BjQawuZ9jr5hQ/J37SJ/+3ascfF49uyJoc8DKLTaW/02BEEQhLvIvCeasPpwIl1qXbKuOGYHAJUbd2O0azVGdS5MHNpsqPynFDsTd7ItfhsDaw4k1KN4sFvfrz4gZ/0GcFG50CSwSbFjrImJ5Pz1FwA+Q4YU2+f7zDDyN2+RA2ogcMJrxZKNCYIgCBVHBNW3IYdD4uBPb9PYYQQLcHK1/OeC9i9Dl7fKFFADGHQGXmr6EpIk3TEBtXH/ARLffBPLuXMAWOPjSXprMunzv8ClXj1y//4blEpCZs3EvX17ADzvuw/P++67ld0WBEEQ7mIKhYK6wQbqBhtK7jwvB9WGmh14pU7NMrW36OgitsVvwyE5eLV58anVga6B+On9nGW0mgY1RafSFTsma/kvYLPh2rw5+nrF84W4tmyBS716mI4cwfDQQ3gPHlzWtykIgiCUkwiqbzMWm4N3l67nrfR1oIAdDd6ndZAE57dD4kFoOgQ6vFrmgPpit2tA7TAaMZ04iS0pEWtiIuZTp8j+YxVIEip/P4ImTcKanEz6119jTUjAmiCvZ6v07lQ8e/a8xb0XBEEQ7nkFmZAiJykjvGyjwdHZ0WyL34YCBYNqDSqxX6FQUM+3HhvjNlLPtx6Da5cMinP+XguA16OPlHp+yJw5GHftxLNPn9v2O4AgCMLdQATVtxGjxcbIH/bT6txitGo76b5Naf3waHlnmzHlbi86O5r3dr3HqIajSkwZu13YMjKIfrS/M1C+mKFfPwInTkBlkEcEvAcNImvZMrJX/oHXgAF4PVLyS4QgCIIg3AhHE7L5aksUfRsF06nmJVO/Y3cDEvhWA/eylZv66eRPAHQI7UCYR1ipx/SI7EGkIZJulbuVKKVlPnsWy5mzoNHg3rlzqedrQ0PQhop7pSAIwo0mgurbRIHFzuCvd3PifAKf6v4FwPe+V66rzfkH57MrcRc6le62DapTZszEmpCA0mBAV70amqBKaCoF4dauPW4tWxQ7Vunigs9TT+Hz1FO3qLeCIAjCvWrVwUR+OxCPxeYoGVSf3y7/LOModb41n9/P/A7AY7Ueu+xxD1R5AEqvxCUvgwLcWrdC5elZpusKgiAIN4YIqm8TCzafZd/5TEa6bMFAvvy0u8a1TW2WJInjGcdZE7UGgNGNRldkV6+JZLWi0GiKbcvfuYvslStBoSB84QL0DRveot4JgiAIwuXJWb/lGVX3169U8oDCzN+El8zOXZpVZ1eRb80nwjOC1sGtr6lPOX+vA8Cze/erHCkIgiDcaCKovk2M6FiV1Ox8Xjy3HvKB1i+AUlnm84+nH2fmnpkkG5NJNaZispsAuK/yfdT2rX2Den115nPnSHx9EuYzZwiaMgXDA70BcFgsJL3zDgDejw0SAbUgCIJw2zoSn0NsRgF6jYrOtfyL77SaIGG//HvlqwfIkiTx44kfARhUaxBKRdnv9RdYYmIwHz8OKhXuXbuW+3xBEAShYpX/X3LhhnDRqHi/ZhQu+XHg6gsNLz8drDS1fGrxSI1HyDBlOAPqILcgxjYeeyO6e1WSw0H6okVE9XuYgoMHceTnk/DKK6R8+CGS3U76wi+xREej8vfD/6WXbkkfBUEQBKEs/lc4St2lVgCu2kvGIxL2g90C7oHgHXnVtkx2E22C2xDoGkjfqn2vqT8Xpn67tmiO2tv7mtoQBEEQKo4Yqb6FzDY7v+6PZ2CzMJSSHbbPlXe0eA40+nK1pVAoaB/Snnld5+Gv98fP1Q+9unxtVBRrYiIJr76Gce9eANzat0dXtSoZixaR/tXXFBw6TMGBAwAETZqEysPjlvRTEARBEK5GkiRWH04EoHeDUqZ+n98m/wxvXabKHHq1ngktJvBKs1dQKVXX1Ccx9VsQBOH2IoLqW2ja6hMs2h5N7NHtvGaZJ5fMUrtA8+FlbiOtIA0XlQvuWncMOsMtT0gmWa3EjnoB8/HjKFxdCZwwAa8B/VEoFLjUr0fiG29i3L0bALcO7fEQJbEEQRCE29jRhIumfl+aoAzg5F/yzyody9XutQbU1oQETIcOgUKBR7du19SGIAiCULFEUH2LbDiZwvLtx5msXs7QmL9BcoCLAR6YA25+ZW7n3R3vcijtEO+3e582wWVLkHIjpS9ahPn4cVQGAxHLl6END3fuM/TujS4ykrixL+IoKCBo8mRRN1MQBEG4rWUZrUT6uVE9wB299pJAOCcR4vcCCqjZ+6ptpRhTSMxPpLZPbbQq7TX1J3edPEqtb9oEtb//VY4WBEEQbgYRVN8CGfkWXl9+gF+0U6itjAEJqN8fenxQ5vqWANvjt/Nv7L+oFCoC9GU/70axREeT9tnnAAS8PrFYQH2BS506VP17LZLJhNLV9WZ3URAEQRDKpV11Pza80qn0nSf/lH+GNgePwKu29Xf038zYM4NOoZ2Y23XuNfVHTP0WBEG4/YhEZTeZJEm8/ushKuUfp7YyBknrDk/+Co98Va6A2uqwMn3PdECucVnNu9qN6nKZSJJE4uS3kcxm3Nq0wdD38slXFEqlCKgFQRBuc/PmzSMyMhIXFxeaNm3Kli1bLnvskCFDUCgUJf7UrVv3Jvb4FjhRGFTXuvooNcDB1IMA1POrd02Xs8TGUrBfzjTucd9919SGIAiCUPFEUH2T/bIvjrVHk2mvPgqAokonqFb+chjLTi4jKjsKHxcfRjYaWcG9LL/sFSsw7t6NwsWFoCnviGndgiAId7Cff/6ZcePG8cYbb3DgwAHat29Pr169iImJKfX4Tz75hMTEROef2NhYfHx86N+//03u+U1UkAVRm+Xfaz1QplMOpR4CoGFA+cpI2rOySPnoI8492BckCX3DhmgqlZI0TRAEQbglxPTvm8hosfHB6uMADPA9B1lAlU7lb8dqZOGhhQCMbjwaT61nxXWyDOx5eWT/8QdSQQEKjRaFRk3Kx3MA8B87Fm1Y2E3tjyAIglCxZs+ezbBhwxg+XE6cOWfOHNauXcv8+fOZNm1aieMNBgMGg8H5+vfffyczM5OhQ4fetD7fKIt3RLN0Vwx9G4UwslPVoh1n1oPDBn41we/qs8VSjakk5CegVCip71e/zNdP//ob0ubPx5GXB4BLgwZU+uD9cr8PQRAE4cYRQfVN5KpVs2R4K5ZsPUnIycPyxsjyZQsF+OnkT2SYMgh1D+Whag9VbCevwpqUROxzz2M+darEPpe6dfF5avBN7Y8gCIJQsSwWC/v27WPixInFtnfv3p3t27eXqY2vv/6abt26Ubly5RvRxZsqIcvEiaRc2uaZi+84vkr+Wbtso9QXpn5X86qGm8atTOfk79xJyqxZAOhq1sT/xRdx79xJzAYTBEG4zYig+iarE+zJ+03z4JgZPILBr3q524jLjQNgRMMRaJSaiu7iZZlOniT2ueexJSej8vfDvU1bJKsFh9mCQqnE/6WXUKjFXylBEIQ7WVpaGna7ncDA4om3AgMDSUpKuur5iYmJrFmzhqVLl17xOLPZjNlcFKjm5ORcW4dvsAKLDQC95qLM31aTPFIN5V5P3dC/7FO/8zbJ08s9evUk5KOPUCjFqj1BEITbkYiAbgKHQyIxx0SIl17eELVJ/lmlI1zD0+bJrSczqNYgqhiqVGAvryx/xw7ixozFkZeHtmpVwhcuQBMSctOuLwiCINxcl46GSpJUphHSRYsW4eXlxUMPPXTF46ZNm8aUKVOup4s3hdFiByheTitqM1jy5IfjlRqXqR3neupyBNX527YB4HnffSKgFgRBuI2Jf6Fvgr+OJtFx5gamFa6n5lxhUH0NU78vqOFdA7Xy5jwTyV65kpjnnseRl4dr8+ZELF0iAmpBEIS7lJ+fHyqVqsSodEpKSonR60tJksQ333zD4MGD0WqvXIf59ddfJzs72/knNjb2uvt+IxRY5aDa9eKg+sT/5J+17ocyBrvjm41nfNPxtAhqUabjrSkp8lIrhQLX1q3L1WdBEATh5hJB9Q1md0jMXncKm0NCp1bK2UIT/5N3VilfUP1vzL/Oqd83gyRJpH3xBQkTJoLViuf99xP29VeoLkpGIwiCINxdtFotTZs2Zd26dcW2r1u3jjZt2lzx3E2bNnHmzBmGDRt21evodDo8PT2L/bkdFVwYqb4w/dthh5Or5d/LmPUb5BHqofWGUsm9bFm78wvXr7vUrYva27vsHRYEQRBuumsKqkXtyrJb+V88Z1LyMOg1DO9QBaK3guQA3+rgGVzmdrLN2UzaOok+v/XhWPqxG9hjmWSzkTT5bVLnfAKA7/BhBH84C+VVRh4EQRCEO9/48eP56quv+Oabbzh+/DgvvfQSMTExjBgxApBHmZ966qkS53399de0bNmSevWurQ7z7ejCSLVz+nfcHshPBZ0BItrdsOvmb5ODarerPMgQBEEQbr1yzx++ULty3rx5tG3blgULFtCrVy+OHTtGeHh4ieM/+eQTpk+f7nxts9lo2LDh3V27spDV7mDO+tMAPN+xCp4umuLrqcvh430fk2/Np4Z3DWr51KrorhZjy8wkYeJE8jdtBqWSwDcm4fPEEzf0moIgCMLtY+DAgaSnpzN16lQSExOpV68eq1evdmbzTkxMLFGzOjs7mxUrVvDJJ5/cii7fMAa9hgAPHZ76wsSgMTvkn1U7g6psyUJ/P/M7aqWatsFt8Xa5+qiz5HCQv0O+jlvbttfUb0EQBOHmKXdQLWpXlt3yvXHEZBjxc9cypE2EvPHcRvlnOdZTb4/fzorTKwCY2GIiSsWNm7Wfs24dSe9MwZ6ejsLFhZCPPsSja9cbdj1BEATh9jRq1ChGjRpV6r5FixaV2GYwGDAajTe4Vzff/CebFt+Qdkb+GVCnzG18cfAL4vPiWXjfQloHX319tPnUKexpaShcXdE3blSO3gqCIAi3Qrmiswu1K7t3715s+71au/Jqvt56DoBRnarhqlVDTgKknQIUZZ4ylmfJ450d7wDwWK3HaB7U/Ib01ZaZSfz4l4kfMxZ7ejraalWp/MMPIqAWBEEQhIulyzPQ8KtWYpfFbiHbnF1sW1pBGvF58ShQUN+vfpkucSHrt1vz5mLZlSAIwh2gXCPVonZl2SVmFxCdbkSjUvBos1B5Y5Rcb5JKDcHVp0ztzN43m8T8RELdQxnXZNwN6WvBwYPEjh6NPTUNVCp8hw/H74VR4kYuCIIgCJdKKwyqfauX2DVn/xzWnV/HjPYzaBLYhN2Ju1kfI9ezruZdDXetO6aTp8jbvAmpoABHgQlHgRF9/QYYHu7nLFnmDKrF1G9BEIQ7wjXVZBK1K6+ukkHP/jfv43B8tryWGopKaZVxPfX+5P0sP7UcgKltp+Kqca3wfmb/708SJ01CsljQVq1K8PTp6OvfPQlmBEEQBOF69P9iO5IEnz/RhEC1EQoy5B2+VYsdtzV+K98f+x6AXEsuSflJvLzpZbLMWYCc/VuSJOJGjsSakFDs3KyffsaanIT/qFE4TCaMe/cB4NZWJCkTBEG4E5QrqL6ZtSvHjx/vfJ2Tk0NYWFh5unpbMLhqaFfdT36Rnwan18q/l3E9dX3/+oxuNJpsS3aFT/uWHA7SPvuctHnzAHDv3JngWbNQubtV6HUEQRAE4U4lSRL7zmfikAo3XBil9gwFbdH9Mr0gnTe3vgnIS7U6hnXEYrdwX+X7nA/HG/g1wBoXJwfUajXeAwag0LvgyMkla/ly0j6di9rfH02lYCSLBXVQENoqVW7m2xUEQRCuUbmC6otrV/br18+5fd26dfTt2/eK55a3dqVOpytP125vDjusGA7GdHm6WBnXU2uUGp5v+HyFd0eSJBImTiTnj1UA+Ax7hoDx41GoVBV+LUEQBEG4U5ltDmdArdeqSl1PLUkSk7dPJt2UTjWvaoxvKg8KaFVaJreeTOOAxuxL3kePiB4UrJZrf+vr1iVo8lvONlQ+PqQvWEDS2++gb9AAkEepyzILUBAEQbj1yj39e/z48QwePJhmzZrRunVrFi5cWKJ2ZXx8PIsXLy523t1Yu/Jyft0fxw87z/NEy8o80jQUNs2EcxtA4woDvwf1rX1gkLl0qRxQazRUeucdvB55+Jb2RxAEQRBuR6bCGtUAeo2q1PXydSFzAABvQElEQVTUq86tYnPcZrRKLTM6zMBF7VKsjT5V+9Cnah8AEvftl9tq0qTYMf7jXsSWkkL2b79R8N9/ALiL9dSCIAh3jHIH1aJ25dWtPZrE/pgs2lf3hzPrYdMMeccDH0NA7auevzdpL7P3zebJ2k9yf5X7K7Rv5tOnSZk5C4DAV18VAbUgCIIgXIbRIgfVGpUCjUoJ6YXltPyKgup10fLo87D6w6jhXeOK7RUckINq16bFg2qFQkGlqVOwpaeRv3kLKBS4tr566S1BEATh9nBNicpE7crLM1ntbD6VBsD94XZY8SwgQdOh0HBQmdpYdmoZh9MOsztp9//bu/P4qOp7/+Ov2TPZISEJYQ07ggiCFVAM0cqirVa9letO1VaKO9bW7bZArXjVclF7AZcWfl6ttbcuxZarYksqCAgiKAgKIhiWQAjZk8ms5/fHSQaHBMhGhmTez8djHuR8z/fMfOaAfvM5361Nk+qQz8f+n92P4fWSMGECXW64vs3eW0REpLPx1PVUux1106PCPdXf2k7LYk7Xyu154vVSgmVleHeaSbl71KgG5y0OBz0XLODgnDk4evfG3qVL67+AiIi0ixYl1XJ8a3YV4/EH6Z4Sx6AN/2GuEtr9LJjyeJOuP+I5wopvzKfePxz8wzaN7fD8/8L75ZfYunYl+7HfaK6WiIjICXjqeqrjnXYIBqDka/PEt3qqn73wWWoDtTisjhO+V03dsG5n377Y09IarWONjyf7P/+z9YGLiEi7UlLdxlZsKwLgqv4Glm0rAAtc9QdwxJ34wjp/3fVXAqEAw9OGMyxtWJvEFCwvpyo/n5K6UQTdf/Mo9m7d2uS9RUREOqtgyCA90UnXBCeUfQMhP9jjzNW/v+XYedSN8RxnPrWIiHR8SqrbUChk8P72QwBcZVtlFuZMiFgl9ITXGyH+90tz642rB1/dqlhqNmzgyB+WUPvFFwQKC8PlXa69hqS8vFa9t4iISCw4q1cqHz9ysXmwo25bzK79wWoFwBPw4La7m/ReNfXzqc9uOPRbREQ6NiXVbeiz/eUcrvSS6LLRd99fzcKR1zX5+rUH1rKvah9JjiSm5ExpcRxGMMiBXzxg7oVZx9GjBwnnn0/G/fe3+H1FRERihmHAt6dJFUdupxUMBbnofy8iOyGbhd9dSEZ8xnHfKuTzUbtlKwDus0efspBFRCQ6lFS3IbvVwpRhWZwZ3IZlz25wJsLQ7zf5+j9/+WcALhtwWZOffDem6l//wn/gALaUFHr+7llcQ4ZgS0pq8fuJiIjEDE8ZLDrPXA/lmj8eLT8SuZ3WF6VfUOmr5IBxgLS4xudI16v9/HMMrxdbly44c/qemrhFRCRqlFS3oeE9Ulh8w2hYthT2AGdcDs6EJl9/+YDL8QQ8/HBQ6xYoK335FQBSf/hvxJ9zTqveS0REJKZ8swYq9kHFPlatWc2zW2yc1z+du4sjt9PaULgBgNGZo7FZbSd8S88nmwBzPrUWCRUR6XyUVLc1Xw1sfdP8eeS1zbr0wt4XcmHvC1v18d6vv6Z6zRqwWEj992ta9V4iIiIx5+Bn4R+TdrzB+t0X0T0lrkFP9YZDZlI9JmvMSd+y5hPNpxYR6cys0Q6gs/jzhr3sLq6GL/4OvkpI7Q29x7d7HKV/fBWAxLw8nD17tPvni4iIdGiFR5PqfoXLsRCii60WqsyFSEkfQCAU4JNDZqJ8TtaJR4QZhoHnE638LSLSmamnug3sL/PwwBufYQDbB75MHMBZ14ZXBz2ZJzc8idvu5oYzbiDFldLiOIJV1ZS/afaSd7mueb3kIiIiQkRPdbK3kNGWHfQI1poFCRkQl8KXxZ9T5a8iyZnE4C6DIy43DAPD68UaZ26z5du9h2BpKRank7hhbbNVpoiInF6UVLeB19YXEDLgkj5B4gr+ZRae9e9NunZf5T7+uP2PBIwA5/c4n5EZI1scR/myvxKqrsaZk0PCuHEtfh8REZGYVFMC5XvNn4d8D774G1fYPiQ1UPeQvH4+9cHjz6cu+cMfKHryKeKGDSPlsu8T8voAiBtxJlans32+h4iItCsl1a3kD4b40wazAb4z7RM4ZECf86BrTpOuf/6z5wkYAcZnj29VQm0YBqWvmKuUdrn2WixN7CUXERGJdb5AiIpaP2mHPsMC0KUvfOfH8MXfuNS2jq+9Pc2KaeZ2WoO6DuJ7/b7Hd7K+0+C9Kt9bAZgrftd+/nm4PH6Uhn6LiHRWSqpb6R/biyiq9NIzPsiQb8xVt5u6QNneir0s27UMgJ+e9dNWxVH94Rp8u3ZhjY8n5YoftOq9REREYoUvEGLQI/8HwPYpO3ADZI2AvhMos6eTGijmjIN/NSvX9VSPzx7P+OyG66YYoRDeneaCZmm33UbNRx/h2bwZgMQLJpzqryIiIlGipLqVXvnoGwDmdf8Ay/4i6JIDZ17dpGsXf7aYoBHkvOzzWtxLHTh8mOLFz1H6Z3OP65QfXI4tMbFF7yUiIhJrnHYrcQ4rtf4QocJPzcLuI8Bq45PkC7mw5M/E+cvM8rqVv4/Hf+AAoZoaLA4H3e64Hcu99+ArKCBYVoZ7xIhT+0VERCRqlFS3QsGRGlbtLCadcs4rModec9EvwX7iOVOVvkrmrJ3Du3veBWDmyJlN/kzDMAgcPozv691Uf7iakpdfwfB4AEgYP470O+9s2ZcRERGJQYZhkBznoNbvxXZoi1mYdRYAF/7wTnjuz0crpw+ksKqQKn8V2YnZJDgSIt7Lu2MHAM7+/bE4HObPvXtD796n/ouIiEjUKKluhfV7Skh02ZmX9H9Yq6oh+2wYdsVJr4uzxbG7fDc2i43bR97OiG4nf3rt3b2bg3PnUrv1c0KVlZHvd9YIMu69l4SxY1v8XURERGLRsk8PUFTpJQ4vrvKvzcLude1y1pnQbSgc3g5WB6T24dXNz7Bk6xKuH3o9v/jOLyLey/vllwC4Bp24R1tERDoXJdWt8G+je3Jl71osi5abBRfPAYvlpNc5bA4eO/8xAqEAw9JPvr1GoLSUvbfNwF9QYBZYrTh69cTVrz+p/3YViRdeiKUJnysiIiKRymr8AAy1FGAxQua2WUlZ5kmLBUb8EP4x11yA1GbnULW5X3VmfGaD96qt66mOGzy4wTkREem8lFS3knXlryEUgAEXQ84Fx61X7a9mS/EWRmWMwmVzMbhr0xpcw+dj/1134y8owNGjBz2ffQbngAHalkNERKQNlFSbW14Ns+4xC7qfFT53z582Eag6i8f7TSVx5JUAHKqpS6oTGibV3h3mImWuQYNOYcQiInK60b5LLWQYBuzbCNveAizw3dknrP9R4Uf8+L0fc83fr2nWZxTOnUvNhg1YExLouWghcWecoYRaRESkjZTVmEn1GZY9ZkH3o1Oy1uw6wt++8vLNd5+DEeYipEU1RUDDnuqQ14tvj/keSqpFRGKLeqpb6C8b9+F+Zx7fAxgxDbKGn7D+R4UfATCq26gmf0bJ0v9H+V9eB6uVHvN/S5waaRERkTZVWjf8e5jV3M2DrKNJtccXBMDtsAHmw+7w8O9jeqp9u3ZBMIg1JQV7RsapDltERE4j6qluoc17y+jnM+dOMfR7J61fn1SPzW7aYmLVH62n6IknAMj8xc9JzM1tWaAiIiJyXKU1PuwEGG7fZxbU9VQbhkGN30yq451mH0SZtwxfyOzZznBHJs7h+dSDBmmdExGRGKOkuoW+2HuQQZa95kH2iXufD9ccZlf5LixY+E7Wd0763sGqagofeggMg5QrrqDLjTe2RcgiIiJyjNIaH/0tB7CFfOBKhtS+APiDBsGQAYDbafZU18+n7hrXFYfNEfE+mk8tIhK7NPy7BWr9QayHPsfuCBGM74YtuccJ63900OylHtJ1CCmulJO+f9ETT+Dfvx9Hjx5kPvywnniLiIicIsOzU8j2H4YKCGQMw241+xvqh37D0eHfqa5U7j777kbfp36PaiXVIiKxR0l1C3x+oIJh7ALA2vPsk26jFR763f3kQ7+rVq2i7M9/BqD7Y49hS0xoZbQiIiJyPI9fNYLdL78IFfDukUwurSv31A39tlstOO1mop2VkMWtZ97a6PscTaq1R7WISKzR8O8W+HRvGSOsXwNgyT77hHUNwwgn1ed2P/eEdYPl5RQ+8h8AdLnhBhLOPflQcREREWmdrhXbANhO33BZrT+I02YND/0+kUBpKYEic1Vw10D1VIuIxBr1VLfAp/vKuNNiJtWcJKkGeObCZ/io8CNGZZx47vWhxx4jcOgQzj59yJh1b1uEKiIiIsdhGAYYIRJLvwDgs0Cf8Lm+6Qns+M1UAsFQuGxHqdkb3TOxJ/GO+HB5/XxqR8+eGmEmIhKD1FPdAgNTDPpZC82DkyxSZrFYGNJ1CDcNuymiAT5W1apVlP91GVitdH98Hla3uy1DFhERkWNsL6zk/F/+GZu/kpBh4TNvZoM6dtvRX5We3PAkVy27ivcL3o+oo/nUIiKxTUl1C9wxpBorBqT0gsRurX6/UG0tB+fMBaDrDTcQP6rpe1mLiIhIy5TV+EgJlgBwhGTKvET0TB+rfvXvzPjI5FvzqUVEYpuS6pY48In550l6qUNGiDlr5/DWV2/hDXqPW6948WL8+/Zhz8oi/c472zJSEREROY7SGj/dLOUAHDZSAajyBgD4eE8Jt/6/Dcx/78tw/aIac970sUl17Q6zTpx6qkVEYpKS6mYqqqwluH+TeXCSpHpH6Q7+suMvzPtoHlZL47fau2sXR37/BwAyH35Ic7FERETaSUmNj3TMpLrEYm55WeExk+p9pR7e317ExoJSAKp8VVT7qwHIiM8Iv4cRCuHd+RUArsGD2y12ERE5fSipbqYHXt/Cgc8/NA96nHiRsvpVv0dnjsZhdTQ4bxgGB2fPAb+fxIkTSfrud9s8XhEREWlcWbWPbpYyAFyp3fnByGzsNnObzJq6fardDnNN1/qh30nOpIg1Uvz792PU1GBxOnH26YOIiMQerf7dDIZhsKeggF4Wc/gX3UeesP6aA2uA42+lVf7WX6nZsAGL203WfzyC5ST7XYuIiEjbKanx0bNu+Pc5w4dwzsVHR6DV71MdX7el1qHqE8+ndvbvj8WuX6tERGKReqqbYV+ph5615rypUNf+4E49bt29lXtZe2AtABf0vKDB+cCRIxQ98QQA3W6fiaNHj7YPWERERI6rrMZPel1STUJGxDmPzxwG7nbUJdX1i5QlRCbVns2fAhCnRcpERGKWHqk2w+a9ZYyo25/aepKh369+8SoGBuOzx5OTkhNxzjAMCn/1K4KlpbgGD6brTTedsphFRESkcX3TEugXVw1+IDETXyBEyDCIc9jCPdXuup7qM9LO4O6z7yYrISt8vb+oiNJXXgEg4YKGD9BFRCQ2KKluho92H+ECq5lUn2iRsmp/NW/ufBOA64de3+B8+V//StX7/wCHg+z/fByLo+F8axERETm17v7uQPiiFoph8cZKHv/j//HIpUO5dUK/8Jzq+uHfg7sOZnDXyIXIDj/9NKGaGuJGjCB56tR2j19ERE4PGv7dDKt3FjMinFQfv6e6xFPC0LSh9E3uy3k9zos45z9wgEOP/gaAbrffTtyQIacsXhERETmJanOdFL87HYAKjx+AWr+5X3X98O9jeT7/nPI3zAfomQ8+gMWqX6lERGKVeqqbaG9JDTVH9pMVV4phsWLpPuK4dXsl9+IPk/9Apa8yYistIxTiwMMPE6qqwn3WWaTdekt7hC4iIiKNCXjBY26ZZUnMBIqpqDXnUs+78kzmXDYMAwOAjYc2kuRMom9yXxxWB0XzHgfDIPnSS4kfdeItNkVEpHPTY9Uminfa+PU5PgAs3YaA8+T7SSc5kyKOS//4KjVr12GJi6P74/O0SqiIiEiU+IMhcmf/GQDD6sCZ1BU42lMN4LRbcdnNnup7Vt7DVcuu4puKb6h8bwU1H3+MxeUi475Z7R+8iIicVpRUN1FaoovJqfvMgxMM/X5719sc8RxpUB6qreXw008DkHHffbhychrUERERkfZRWuMjOVhmHiR0I9ntAqCi1t+gbm2gljKvWTfDnkrRk08CkHbLzTiys9sjXBEROY2pq7Q59q43/+z1nUZPf1X6FQ+tfgi33c2Kf1tBiislfK7qn/8kVFmJPbs7Xa67tj2iFRERkeMorfbTzVIGgCUxg2S3uWhohccc/v3EO19woMzDrRP6kZxkbrvltrsJ/v0f+Pftw56RQdotmsYlIiLqqW6S3cXVvL5hN8a+j82CXuc2Wu9/tv8PAOdlnxeRUAOUvfUWACmXX67FTERERKKstMZHt/o9qhMzSY4zk+pyj59dZbt4f8dXvLX5AEeqfUf3qI7PpOLtvwHQ9aabsCacfCqYiIh0fuqpboLlWwp5573/4yqXB+JSIH1Qgzp7K/ay7KtlANw0LHLf6cDhw1Sv/hCAlMsuO/UBi4iIyAmVVvtIpz6pziArJY4Lh2SQnurhh8v+jWCiA3iYeKeNg9UHARhQm4pn48dgsZB86SXRC15ERE4r6jJtglU7DzPausM86HUuNNLTvPizxQSMAOf1OI+RGSMjzpX/7e8QCuE+6yzNpRYRETkNlNYcHf5NYgYDMhL5w/RzmDKwnGefqeUXf6kCw4rbYaOoxtx2a8zntQDEjx6NIysrSpGLiMjppkVJ9cKFC8nJySEuLo7Ro0ezatWqE9b3er08/PDD9OnTB5fLRf/+/fnDH/7QooDbW40vwMZvSr+VVDecT/11+df87WtzONgdI+9ocL68fuj3FT84VWGKiIi0qc7e1pvDv8vMg8TMcHnl+nV0rYJRXxucc+hL3E5bePj3gI/NHuvk713a3uGKiMhprNnDv1977TXuueceFi5cyHnnncdzzz3H1KlT2bZtG7179270mquvvppDhw7x+9//ngEDBlBUVEQgEGh18O3ho90l+IMG33HsNAsamU+9aPMiQkaIvF55DE8fHnGu9osv8H75JRaHg+QpU9ojZBERkVaJhba+W6KLnLga8AOJGeFy3/Yd4Z+v/XIFbsdMLu5zMX1KbSTvWQp2O0mTJ7d/wCIictpqdlI9f/58brnlFm699VYAFixYwLvvvsuiRYuYN29eg/rvvPMO//rXv/j666/p2tXcA7Jv376ti7odrd5ZTHeOkMkRsNigx+iI8yEjRJo7DZfNxe0jb29wffmbbwGQeOGF2FJT2yFiERGR1omFtv7qc3rBWg+UAAlmUj3m0fe567Pd4TpDSgt4+eXb+Pkd/0PfHesoBhLOG4+9S5foBC0iIqelZg3/9vl8bNy4kUmTJkWUT5o0iTVr1jR6zbJlyxgzZgxPPPEEPXr0YNCgQfzsZz/D4/Ec93O8Xi8VFRURr2hZvbOYMdYvzYOsM8EZudKn1WLlge88wD9++A8Gdx0ccc4IBCj/mzksPOXyy9slXhERkdaIqba+ypwrXT/822Gz0K/YjHl7t3QABr75MduLt1Hx978DkHKphn6LiEikZiXVxcXFBINBMjMzI8ozMzM5ePBgo9d8/fXXrF69mq1bt/Lmm2+yYMEC/vKXv3D77Q17devNmzePlJSU8KtXr17NCbPNlFT72FFUyWhr3dDv3mOPW/fYLbQAqj/8kOCRI9i6diVxwvmnKkwREZE2EzNtva8GfJXmz3XDv7NDFaRVhQgBYxY9TcBuZch++N/Z1+P75hssLheJF17UvnGKiMhpr0ULlVkslohjwzAalNULhUJYLBZeeeUVvvOd73DJJZcwf/58li5detwn2A8++CDl5eXh1969e1sSZqt1TXCy4eHvcmW3/WbBMYuUrT2wlk8OfYIn0Pj3KKsb+p38vUuxOBynMlQREZE21dnb+pueNUeShWwucCUBMKByHwCl3RLoNWwU9svNudNXvW9+h8S8idgStTe1iIhEalZSnZ6ejs1ma/CkuqioqMET7Xrdu3enR48epKQc7ckdOnQohmGwb9++Rq9xuVwkJydHvKIl3eEnuWy7eXDMImXzN87npnduYvX+1Q2u8x86ROX77wOQesUVpzxOERGRthArbX2o0lzROxifAXUPC/qXmMPBrf3PwWKxMODOn+O3Hf1lKeV732vXGEVEpGNoVlLtdDoZPXo0K1asiChfsWIF48ePb/Sa8847jwMHDlBVVRUu27FjB1arlZ49e7Yg5HZ24BMwgpDcE1KOxlsbqGVnqTksfHja8AaXlf7xVQgEiB8zhrihQ9stXBERkdaIhbY+EAwR7z9iHnxr5e8exWZv+a7Unsx6bTPPflZO8JJcALxuOwkXXNDusYqIyOmv2cO/Z82axYsvvsgf/vAHtm/fzr333ktBQQEzZswAzOFcN954Y7j+tddeS1paGj/60Y/Ytm0bH3zwAffffz8333wzbre77b7JKfDQm1tY/U9zeNixQ7+/KPmCoBGka1xXshKyIs6FPB7K/vQnALrcdCMiIiIdSWdv68s9ftIpB8CWfLQNTz/4FQCb4jJ4Y9N+3tt2iOH3z8U2+iy63zsLq9MZlXhFROT01uwttaZNm8aRI0eYO3cuhYWFDB8+nOXLl9OnTx8ACgsLKSgoCNdPTExkxYoV3HnnnYwZM4a0tDSuvvpqHn300bb7FqeAxxfk1fUFXGxfBzYaDP3+/MjnAAxPH95gjln5srcJlpfj6NGDpAsvbK+QRURE2kRnb+tLa/x0s5QBYK3rqQ6Wl5NcYvZeH8g5AruzcTttODIyGPTKn6IVqoiIdADNTqoBZs6cycyZMxs9t3Tp0gZlQ4YMaTCM7HS363AVGCFG28yn1vSOTKq3Fm8FGg79NgyDkpdeAqDLDddjsdlOfbAiIiJtrDO39aU1PrrV9VTXb6dVu/0LAIpSYNywUazbHSDeqTZcREROrkWrf8eCLw9WMsBygGSqwREPmZHJc31SPSx9WER59Ydr8O3ahTU+ntSrrmq3eEVERKRpSqt9pFvqk2qzp7r6888A2J1lIcFqDgl3O5RUi4jIySmpPo4dhyo5y7rLPMgeBbajW2JV+irZU7EHMId/f1vJS/8PgJSrrsKWlNQusYqIiEjTxTvt5MTVLapWl1SXbvkEgH3dnRiBRADczhYN6BMRkRij1uI4dhyq5FxL3f7UmZG90XH2OP5n6v/wVdlXdI3rGi73fv011R+sAouFrtdf157hioiISBOdPzAdEmqgjPDwb9+2L7ADX6Sm8OJycyi426G+BxEROTkl1cex41AV19cn1d0GR5xzWB2MzBjJyIyREeWlr/wRgMS8PJx1i7mIiIjIacYwoOqw+XNiBqGaGmx7zX25v0rNMJNtzB5tERGRk1Fr0QiPL0hJtY+Bln1mQbchJ73GMAyqVq4EIPWH/3YqwxMREZHW8FZCwGP+nJBB7dYvsRhQkggljizsVgsfPXQRdqt6qkVE5OTUWjTC7bTx+cPn09ta9xT7mKT6mU+e4a9f/ZUaf024zL93L/4DB8DhIOE7kXtai4iIyOljzp/Mh+B+ewI446ndtg0AX/9eBKsHEggZJLsdpMQ7TvQ2IiIigHqqj8tastP8IT4dEtLD5Uc8R3hhywtYsHBR74vC5dVr1pjVzzoLa0JCu8YqIiIiTRcoPwSALy4dB1C73Uyqzxj/PYKF/QGo8PhJS3RFK0QREelA1FN9PIe/NP88ppf68yOfA5CTkkOiMzFcXr1mLQAJ541vn/hERESkRewecyRaML4bALXbtgMQP+wMklxmf8M9r23mk4LS6AQoIiIdipLqRjz4xhbey/+XeXDMImX1+1N/eystIxik+qOPAEgYN659ghQREZEWifMWmz8kZmL4fHh37gCgsm83kt3mkO9VO4vZU1wdrRBFRKQDUVLdiBXbDmEpNrfTOLanuj6pHpZ2dJut2m3bCJWXY01KIm545L7VIiIicvoIhgwS/CUA2JMzCZaXQyBIyAJ3b/s15/Y7ulVmvNMWrTBFRKQDUVJ9jJJqH8VVXgY2sp2WYRjh4d/f7qmuH/odf+53sNg1TV1EROR0VVbjI71uzyxXaneCVVUAeJzQO7kP868eybDsZADiHEqqRUTk5JRUH2PHoUpc+OhtLTILMoaGzxVWF1JSW4LdYmdw16PJdvXauvnUGvotIiJyWiup9tHNUg6ALSmTUJU5xNvjgt7Jvc2ffUFA+1SLiEjTKKk+xo5DlfS3HMCKAe4ukNAtfO7LEnPxsoFdBuKymSuChjwePBs3ApAwXouUiYiInO76uszeaRIzCVWbP9c4oXdSXVLtN5Nqt3qqRUSkCfQI9hhfHqxkQHjo9xCwWMLn8nrnsfLqlZTUloTLajZ+guH3Y+/eHWffvu0crYiIiDTHwMwkiK+BCiAxg9A+c4XvWqfZU/3b976ksLwWALfmVIuISBOop/oYOw9VMci6zzw4ZuVvgHR3OoO6DAofV68196dOGDcOy7cScBERETlNeeq2yorviq/CHApe47LQJ7lPRFuupFpERJpCSXUjBlnre6qHnrgi39qfWkO/RURETn+GAf4a82dHPFVl5hoqtS4LaXFpJMeZg/hG9kolM8kVrShFRKQDUVJ9jD/PGMfF6WXmwbd6qr8q/YrbVtzGH7b+IVwWKCnBu307AAljz23PMEVERKQF5r29+eiBPQ67xw9Av+xhWCyW8D7VqfEO7Db9miQiIien1uJYAS+W0q/Nn7+1R/Wmw5tYc2ANaw6sCZfVrFsHgGvwYOzp6e0apoiIiDRfWUXF0QOHG0dtAIDBPUcCkBxnJtUVdcm2iIjIySipPlbxTjBC4EqBpKxw8ZbDWwAYkT4iXFa5Mh/Q0G8REZGOorq6EoCQxQ42B6G6faptiYkA+IMhAD4pKItKfCIi0vEoqf6W/7dmD0++8lfzoNvgiJW/txSbSfWZ6WcCEKqupvL99wFInjypfQMVERGRFqmuS6JDdVtjVpUdBqDSbm6jlZbgjE5gIiLSYWlLrW/58lAlmaU7zbuScXTod5Wvil1luwA4s5uZVFf+4x8YHg+OPr2JO+usaIQrIiIizeT1mEm14YgH4FDxHroC/ypZTz9gXP80fj5lMIMykqIXpIiIdChKqr+lsMzD+d/eo7rO50c+x8AgOyGbdLc5d7r8r8sASPn+ZdpKS0REpAMwDAOvpwYcYHHEmWXV1QA4kpIBsFgszJw4IGoxiohIx6Ph399SWF7LIEvDPao/O/wZcLSX2l9URPVacyutlMu+375BioiISItUeAI4DC8AVqcbAEu1BwBncmq0whIRkQ5OSfW3FJVV0tdy0Dz4Vk+1N+glwZEQnk9d8fflEArhHjkSZ+/e0QhVREREmskbCHJWpjln2lo3/Nvq8QEQl9wlanGJiEjHpuHfdaq9AdK8+7C7QhjORCzJPcLn7hh1BzNHzsQfMrfXKF9WN/T78suiEquIiIg0X0ZyHA9e3Bf+DDjMnmp7XVIdn5wWvcBERKRDU091ncJyD70sRQBYuuZErPwNYLVYcdlc1O7YgXf7dnA4SJoyJRqhioiISEv5a80/7eacaketuep3Qmq3aEUkIiIdnJLqOjW+IGcmmSuCktIrXB4yQhH1Kt5+G4DECy7A3kVDxURERDoUf435pyMeIxjE5TPb+eQumVEMSkREOjIl1XVG9Ezl3nPM+VV8a+j3f238Ly5941Le+uotjFCI8rf/BkDK97VAmYiISEfy/Ae7+O3yT80DRxyhupW/AbIycqIUlYiIdHRKqr+tvG7l75Se4aLPDn9GQWUBFizUrN9A4OBBrElJJOZNjEqIIiIi0jKHKrwEvOZq39jd4aTa4nCQlpwVxchERKQjU1L9bRV1e1TXJdX+kJ9tR7YB5nZalf/8BwBJky7G6nJFJUQRERFpmZJqH3EWc0stHG5CVea0L2tiYhSjEhGRjk5JdZ07/vgJB/d+ZR7UDf/+qvQraoO1JDmS6Jvcl+oP1wCQeEFutMIUERGRFjpS7SMOc7VvHHFUlpkLlAbj9aBcRERaTkl1ne37y+gaLDYP6nqqvyj5AoAz0s4geKgI365dYLWSMPbcaIUpIiIiLVRS7T2aVNvd7D20E4ADoZIoRiUiIh2dkmrAMAx8FQdxWoIYFiskdQfgm4pvAMhJyQn3UsedORxbSkrUYhUREZGWKany4Q73VLvxlJkP0wNuZxSjEhGRjk5JNVBW46dr4LB5kJgJNjtwNKnum9KX6g8/BCBh/PioxCgiIiItZxgGxdU+4ixHk+railIAgm4N/xYRkZZTUg0cKPeQbTkCgOVbe1TnpORwZvqZDEjuR/XatQAknndeVGIUERGRlvMFQ4zqlUqay9yXGnscvooy8+cEd9TiEhGRjk9JNXCgrDacVJNydI/qu86+iz9e+kdGlCUTLC3FGh+P+6yzohSliIiItJTLbuO128ZxXu94s8ARj7+qEgBLQnwUIxMRkY5OSTVQWO6he31SndyjwfnqNeZ86vhzz8XicLRnaCIiItKWArXmn444gnVJtTVBW2qJiEjLKakG7FYrA11l5kHd8G9v0EsgFAAIL1KWoKHfIiIiHZvfY/5pdxOqrjZ/TEqKYkAiItLRKakGrj23N7lZdQuX1A3/fn3H65zz8jnM+9dsPBs3ApBwnhYpExER6Yje3LSPMY++T1FJmVngcNPXlglAVrec6AUmIiIdnpLqeuX7zT/rhn9/U/ENASNAj6/KMfx+7NndcfbtG734REREpMWKKrwUV3mxheqHf7vpZiQA0CNzQBQjExGRjk5JNUDAB1WHzJ/rhn/Xb6fVb0cFYK76bbFYohKeiIiItE5JtTkizWV4zQJ7HMHqKkBzqkVEpHValFQvXLiQnJwc4uLiGD16NKtWrTpu3fz8fCwWS4PXF1980eKg21IoZHDlE38BDAybCxLSAdhTsQeALp8VANqfWkREpCM7UpdUO42j+1RXlx0GwNCWWiIi0grNTqpfe+017rnnHh5++GE2bdrEhAkTmDp1KgUFBSe87ssvv6SwsDD8GjhwYIuDbkvFVV7slQfMg+RssFjwBX0cqDpAl0oD6+59YLEQP3ZsdAMVERGRFqvvqbaHzJ5qv83O4WLzdxefSwP3RESk5ZrdisyfP59bbrmFW2+9laFDh7JgwQJ69erFokWLTnhdRkYGWVlZ4ZfNZmtx0G1pf5knvEe1JaUnAHsr92JgMOKgC4C4oUOxd+kStRhFRESkdY5U+7ATwGqYO3uUh/y460aCJ6R0i2JkIiLS0TUrqfb5fGzcuJFJkyZFlE+aNIk1dXs5H8+oUaPo3r07F110EStXrjxhXa/XS0VFRcTrVCksrw0n1dQl1fVDvwfUpgDgzNGqoCIiIh1ZSbWXOHzh44qQH3f9SPDk5ChFJSIinUGzkuri4mKCwSCZmZkR5ZmZmRw8eLDRa7p3787zzz/P66+/zhtvvMHgwYO56KKL+OCDD477OfPmzSMlJSX86tWrV3PCbJYDZR661yfVdSt/d3F1YWrfqQwJmk+u7cd8XxEREelYBmYkMTTdUXdkodxTgSNoHlkTEqIWl4iIdHz2llx07CrYhmEcd2XswYMHM3jw4PDxuHHj2Lt3L0899RQXXHBBo9c8+OCDzJo1K3xcUVFxyhLrwvJaxoV7qs2k+uzMszk782z2vXkvlXyGI0tJtYiISEf2h+nnQGk3eBqwx1FRfpi4unNKqkVEpDWa1VOdnp6OzWZr0CtdVFTUoPf6RMaOHcvOnTuPe97lcpGcnBzxOlUOlHnoEU6qIxP3wCFzmy17hpJqERGRDs9/dI/q6rIiAHxOK5bTZJ0XERHpmJqVVDudTkaPHs2KFSsiylesWMH4Zmw5tWnTJrp3796cjz5luiW56GEtMQ/qhn8fqDpAMBTEf8h8eKCeahERkU4g4DH/dLipLjMfqAfcjhNcICIicnLNHv49a9YsbrjhBsaMGcO4ceN4/vnnKSgoYMaMGYA5dHv//v289NJLACxYsIC+ffsybNgwfD4fL7/8Mq+//jqvv/56236TFpo7pS9sqjIPUnpQ6atk8uuTcVkc/E9R3fYbmlMtIiLSYa3eWcw9r23i3zP28TMAh5shrt4AOJJSohqbiIh0fM3eUmvatGksWLCAuXPnMnLkSD744AOWL19Onz59ACgsLIzYs9rn8/Gzn/2MESNGMGHCBFavXs3f//53rrzyyrb7Fq1Rsd/805UMcSkUVJix9/AnQiAIViv29PQoBigiItL+Fi5cSE5ODnFxcYwePZpVq1Ydt25+fj4Wi6XB64svvmjHiI/vcFUtxVU+Ar4as8Dupq/DfGCelJoRxchERKQzaNFCZTNnzmTmzJmNnlu6dGnE8c9//nN+/vOft+Rj2kf5PvPPuqHf9dtpDQllAoexp6VhcWhomIiIxI7XXnuNe+65h4ULF3Leeefx3HPPMXXqVLZt20bv3r2Pe92XX34ZsQ5Kt26nx/7PR6rMkWddnSGzwBFHqKoaAGtiYrTCEhGRTqLZPdWdydeHq3jsT++bB3Urf39T8Q0A/X2pgIZ+i4hI7Jk/fz633HILt956K0OHDmXBggX06tWLRYsWnfC6jIwMsrKywi/babIAWEl1fVIdMAscbgqLdgFguF3RCktERDqJmE6qyz1+EmrrVjJP6Qkc7anu6XEDYNciZSIiEkN8Ph8bN25k0qRJEeWTJk1izZo1J7x21KhRdO/enYsuuoiVK1eesK7X66WioiLidarUJ9Wp9RtT2928+/mbAJTavKfsc0VEJDbEdFJd7Q3SnbrttJLNpLq+p7pbtXlrHNpOS0REYkhxcTHBYLDBVpmZmZkNttSs1717d55//nlef/113njjDQYPHsxFF13EBx98cNzPmTdvHikpKeFXr169jlu3tY7UJdXJ9rqk2hGHUWOuBO44hdt2iohIbGjRnOrOosrrp3t4j+oeGIYRTqqTywOEAHtWVvQCFBERiRKLxRJxbBhGg7J6gwcPZvDgweHjcePGsXfvXp566ikuuOCCRq958MEHmTVrVvi4oqLilCXW9T3VyTa/WeCIx1qXVLuSupySzxQRkdgR40l1kIHhpLongVCAa4dcyzcV3+BaeQQP4MjUqqAiIhI70tPTsdlsDXqli4qKGvRen8jYsWN5+eWXj3ve5XLhcrXPfOYeqW5qfEGSHeacar/Nid1jJthxyUqqRUSkdWJ7+Hetn+z6pDq5Bw6bg7vOvovfTvwtwUNFANgz1VMtIiKxw+l0Mnr0aFasWBFRvmLFCsaPH9/k99m0aRPdu3dv6/Ba5JlrRvF/d08gO8E8rrTbiTM7r3GndI1eYCIi0inEdE+1v7oEt6WuVa3bUgvMIW7+Q4cAsKunWkREYsysWbO44YYbGDNmDOPGjeP555+noKCAGTNmAObQ7f379/PSSy8BsGDBAvr27cuwYcPw+Xy8/PLLvP7667z++uvR/BoN+c0h3+VWG+669ckcSZpTLSIirRPTSXW61dyj0md143TEcbjmMEEjSJeAC6OmBgCHttQSEZEYM23aNI4cOcLcuXMpLCxk+PDhLF++nD59+gBQWFhIQUFBuL7P5+NnP/sZ+/fvx+12M2zYMP7+979zySWXROsrNK4+qbZYiPcaAFgTEqIZkYiIdAIxnVT/YFgKrAZnfAoAL255kT9+8UfuSr2S8wFrcjLW+PjoBikiIhIFM2fOZObMmY2eW7p0acTxz3/+c37+85+3Q1TNZxgG5z3+T5x2K+/2qcEFZMSlErB2BUqwJiZGO0QREengYjqpxltl/ukyG9SD1eaiLN09TkCLlImIiHR03kCIA+W1AFh7mD3V2fEZVAddBABrgpJqERFpnZheqAxfXVLtNBvUQzXmPOr0SnPLEC1SJiIi0rHV+oPhn20hM7nG7iZUZf4OYE3U8G8REWmdmE6ql+ZvAaAsaG7pUd9TnVJhbrmhRcpEREQ6Nk9dUu2wWbD6zaR6X7DmaFKtOdUiItJKMZ1UV1WUA+C3J+AP+jlSa26v5S6tX6RMPdUiIiIdmcdnJtVxDhsEzOHfr+z/CAxzoTKb5lSLiEgrxXRSbfdXmj+4kijymPtSO61OrMVmsm3Xyt8iIiIdWn1Ptdthg7qe6hqP+fDcsFqwuN1Ri01ERDqHGE+qzS21rK6k8NDvzIRMAnV7VDuylFSLiIh0ZPVzqt1OG/jNZNpbbf4ZcruwWCxRi01ERDqHmF792x6sAQvY3EmkxaXxo+E/It4eT+DQ/5jn1VMtIiLSodmsVgZnJtE9NQ5KzJ5qf7U5DJx49VKLiEjrxWxSbRgGrlA12MDuTqZvSl9mjZ5FyOfjy5KnASXVIiIiHd3IXqm8e+8F5sHjZjLtrzFHqlm08reIiLSBmB3+XeMLkoD5xNoZnxwuDxSZc6stTie21NRohCYiIiKngt+DAfirzaRai5SJiEhbiNmeam8gRIbTB0FwxCezu3w3brubhAP7AbBnZWmelYiISGcRCkHQS6HdhrXKB0BcUpcoByUiIp1BzPZUd01wMraHuT+1xZXEg6se5OK/XMzW7R8A4MjQHtUiIiId3esb93HRb/N58u+fAuAOGVyekQeAPSn5RJeKiIg0Scwm1QD46rbUch5d/Tu10lwlVPOpRUREOr7iKi+7DldTUWlul9klFOLs5OEAWDWnWkRE2kBsJ9XeKgD8jjiO1B4BIL7UXMTEru20REREOrz6faqTbAGzwOYkVLelljVBSbWIiLRezCbVq3cWU1pWCsChkDm3yml1YjtiPsl2qKdaRESkw6tPqhNtAQzg7aRkSo+Y66dooTIREWkLMZtUH66qxR0yV/88FDJ7pzMTMgkcPASAPTMrarGJiIhI26j11SfVfvba7TzUJZ4PdrwLgDVBSbWIiLRezCbV1R4vcRY/AAcD5jDwrIQs/EVmUu3I1EJlIiIiHV19T3W8xc/nLicA3Qwzmbaqp1pERNpAzCbVvpqK8M8HveaQ78y4DAJFhwFzSy0RERHp2Dz+EAAJ1qNJdZdQHKA51SIi0jZiNqn21yXVAYuDszLP5ubhN5OXdDYEAmC1Yk9Pj3KEIiIi0lqpbgc9Ut2kOvxsdZpJdbLfDmj1bxERaRv2aAcQLQGPmVT7bPGMyRrDmKwxeLZ+zh7AnpaGxR6zt0ZERKTT+PUPhvNrILj1de45aCbVcV4D0EJlIiLSNmK2pzpUa+5R7bcfbVCDR4oBsHVTL7WIiEhn8k3VAWqsVtxYsHnMXT80/FtERNpCzHbHptpqAQg5Evjs8Gd0c3cjrsTcq9reNS2aoYmIiEgb21q5B4ChxBEqN9dSsSYlRzEiERHpLGI2qb7h7DT4ChJSu3Dd8usA+D/PbQDYunaJZmgiIiLSRm5euoHiKi9P9Uvjvw8WYe86FsO/A2w2HFmZ0Q5PREQ6gZhNqvGa22gVOeMgBC6bC0eFuV+1eqpFREQ6h+2FFRSW15Lc12CQp5aa6iS+ARxZWVo/RURE2kTMzqnGZybVB+sa1Mz4TIIlJQDYunaNWlgiIiLSdur3qXYa5rQvf4V57OjZM2oxiYhI5xKzSfWf12wHYI9hASAzIZNAqZlU29OUVIuIiHQGHl8Qi+MIS6q3sNodh6/MD4CjZ48oRyYiIp1FzCbVleVmAn3IYm6rkRmfSfBIXU91FyXVIiIiHV0oZOANhLDFf81S7y6WpCTjLzV7rJ29ekU5OhER6SxiMqk2DANnsAaAYlsIgKyELAL1q3+rp1pERKTDqw2YQ71t7n0ADPP68B8x239HDw3/FhGRthGTSbU3ECIec1GyYos5DCzTnUGwpBTQnGoREZHOwOOrS6rjDgBwhs+Hr9hcU0XDv0VEpK3E5LKXVd4AiZjDvy7tMpx+/b7LiMRBGLVmmV1JtYiISIcXCBl0T4mj2lGNAXT3BgiUVgLg1EJlIiLSRmKyp7qqNkBCXU/1lIwx3Dv6XgZYzL0qLS4Xlvj4aIYnIiIibSAzOY61D15ESoI51Suh0gIGWOLisKWnRzk6ERHpLGIzqfYGSLSYSTWuRACCR8z51La0rlgslmiFJiIiIm3IMAyq/OaQb1el+WuPo0cPtfUiItJmYnL4dzBkkGL14rXAV94jxJfvJr1uj2p717QoRyciIiJtxRfyEQgFAHBWmom05lOLiEhbalFP9cKFC8nJySEuLo7Ro0ezatWqJl334YcfYrfbGTlyZEs+ts2c1SuVnCSD/XY7/77pP7l++fUE65JqW9cuUY1NRERE2sbGb0q4evF6Loh/nP+pdkCVDQCnVv4WEZE21Oyk+rXXXuOee+7h4YcfZtOmTUyYMIGpU6dSUFBwwuvKy8u58cYbueiii1ocbJvyVeGxmF/fbXcTUE+1iIhIp3K40seneys4WNyFkV4fgWpzgJ5Di5SJiEgbanZSPX/+fG655RZuvfVWhg4dyoIFC+jVqxeLFi064XW33XYb1157LePGjWtxsG3GMMBXRY3VHAYW74gneKS+p1orf4uIiHQGtX5zSy23wwb+Gnx1PdUa/i0iIm2pWUm1z+dj48aNTJo0KaJ80qRJrFmz5rjXLVmyhF27dvGrX/2qSZ/j9XqpqKiIeLWlv27YCUYIT90iJfH2eIKldT3VaUqqRUREOgOPP4jFUcwRx9952xHCX103/LtXryhHJiIinUmzkuri4mKCwSCZmZkR5ZmZmRw8eLDRa3bu3MkDDzzAK6+8gt3etHXR5s2bR0pKSvjVq40bv0OHiwGospqNq9vuJlDfU91FSbWIiEiHZhhQeRC/pwqb6xD7eJPXHU6C3vqeag3/FhGRttOihcqO3YbCMIxGt6YIBoNce+21zJkzh0GDBjX5/R988EHKy8vDr71797YkzOPye8ye70qbCzCHfwdKzC211FMtIiLSwS39Hvx2MGlFa8HqBSC71Nyr2pqchC0pKZrRiYhIJ9OsLbXS09Ox2WwNeqWLiooa9F4DVFZW8vHHH7Np0ybuuOMOAEKhEIZhYLfbee+997jwwgsbXOdyuXC5XM0JrVlCtZUAVNcn1fZ4giWlgOZUi4iIdHgpZk90SsWXWGxOADLKzVNO9VKLiEgba1ZPtdPpZPTo0axYsSKifMWKFYwfP75B/eTkZLZs2cLmzZvDrxkzZjB48GA2b97Mueee27roW6g+qR4YiuPHZ/6YC3vlETxS11OtpFpERKRjyxwGQIZnF3FOHwDpdUm1o6fmU4uISNtqVk81wKxZs7jhhhsYM2YM48aN4/nnn6egoIAZM2YA5tDt/fv389JLL2G1Whk+fHjE9RkZGcTFxTUob0+G10yqz7AkMuHsuwhWVbHD7wfUUy0iItLh1SXVg4xvuOWCCfx+K6SGk2r1VIuISNtqdlI9bdo0jhw5wty5cyksLGT48OEsX76cPn36AFBYWHjSPaujzeIzk2rDkQgQ7qW2xMdjdbujFpeIiIi0gcy6B/clu6jylgGQXGGu/aLttEREpK01O6kGmDlzJjNnzmz03NKlS0947ezZs5k9e3ZLPrbNJFpqATjkclFVvoekogMA2Lt0iWZYIiIi0hYSMyA+HWqKqa402/iEuqRac6pFRKSttWj1747ulnO6AbDUXc733/o+az9/BwBbWlo0wxIREZG2YLFAltlbnbYjhV92v434cE+1kmoREWlbMZlU460CwGMxv358VQDQImUiIiKdRt0Q8LRD39CrKh4CZpvv6KHh3yIi0rZaNPy7w/OZSXVN3dbacZXmHpZapExETiYYDOKvW9hQOjeHw4HNZot2GNJSdYuVDbHsxX3kMAD2RBvWU7hlp4h0DqFQCJ/PF+0wpB20VVsfc0m1LxBi1ae7uAioJgSAqy6ptqcpqRaRxhmGwcGDBykrK4t2KNKOUlNTycrKwmKxRDsUaa66pHpb6iHcBz5mFOBIdUQ3JhE57fl8Pnbv3k0oFIp2KNJO2qKtj7mkutobwFNVBjbw1CXVjooaAGxdlFSLSOPqE+qMjAzi4+OVZHVyhmFQU1NDUVERAN27d49yRNJs6YMJYOWlLnFM2LnNTKq7aIcPETk+wzAoLCzEZrPRq1cvrNbYnCkbK9qyrY+5pLrKGyARc/VvjxEEwF5Wbf6pnmoRaUQwGAwn1Gla0DBmuOu2WCwqKiIjI0NDwTsaRxzf0IMqC2SUmw/RnWnxUQ5KRE5ngUCAmpoasrOziY/X/y9iQVu19TH3+KXKGyDB4gGgJmTOi7SWm/tWa061iDSmfg61GtjYU/93Hovz6BcuXEhOTg5xcXGMHj2aVatWNem6Dz/8ELvdzsiRI09tgE3wOb3wWS10KzOPHWmJUY1HRE5vwaDZ4eZ0OqMcibSntmjrYy6prv5WT/U12Rdw7ZBrsZQpqRaRk9OQ79gTq3/nr732Gvfccw8PP/wwmzZtYsKECUydOpWCgoITXldeXs6NN97IRRdd1E6RntgXNnOl77RKAwBHenI0wxGRDiJW/98fq9ri7zvmkuoqb4AEzJ7qOwZO44HvPECotAzQlloiIiIA8+fP55ZbbuHWW29l6NChLFiwgF69erFo0aITXnfbbbdx7bXXMm7cuHaK9MSuudxM7hPMZ+lYk9RTLSIibS8mk+rEuuHfuBIJVVRAwNynWj3VIhLr8vPzsVgsJ1zlfOnSpaSmpp70vSwWC2+99VabxSbtw+fzsXHjRiZNmhRRPmnSJNasWXPc65YsWcKuXbv41a9+dapDbLKqlGwA3HU749gSlFSLiKitb3sxl1QHggaJ1OIHCnwVHC7cBYA1IUF7V4pIp7J48WKSkpII1D04BKiqqsLhcDBhwoSIuqtWrcJisZCdnU1hYSEpKSlN/pzZs2e32fzZ2tpapk+fzplnnondbucHP/hBm7yvNF1xcTHBYJDMzMyI8szMTA4ePNjoNTt37uSBBx7glVdewW5v2hqoXq+XioqKiFdbq3LGYzEMXD5z+Lc1KanNP0NEJJrU1p8eYi6p/sGZ3XBaAux12Ln0H7fyi7/OAMCmFX1FpJPJy8ujqqqKjz/+OFy2atUqsrKy2LBhAzU1NeHy/Px8srOzGTRoUFT3ZQ4Gg7jdbu666y6++93vRiUGMR37b8AwjEb/XQSDQa699lrmzJnDoEGDmvz+8+bNIyUlJfzq1atXq2P+tkMVtSz4v2p+cygJK2bc1iTNqRaRzkVt/ekh5pJqfFUAeCzmV0+rdQCaTy0inc/gwYPJzs4mPz8/XJafn8/ll19O//79I4by5ufnk5eX1+iQsKVLl9K7d2/i4+O54oorOHLkSMS5OXPm8Omnn2KxWLBYLCxdujR8vri4mCuuuIL4+HgGDhzIsmXLThhzQkICixYt4sc//jFZWVmtvgfSfOnp6dhstga90kVFRQ16rwEqKyv5+OOPueOOO7Db7djtdubOncunn36K3W7nn//8Z6Of8+CDD1JeXh5+7d27t02/R1mNnw93VOMtMYeAYzGwuBPa9DNERKJNbf3pIfaSaq+50neNIw6AtFpzmJrmU4tIS9T4Asd91fqDbV63uSZOnMjKlSvDxytXrmTixInk5uaGy30+H2vXriUvL6/B9R999BE333wzM2fOZPPmzeTl5fHoo4+Gz0+bNo377ruPYcOGUVhYSGFhIdOmTQufnzNnDldffTWfffYZl1xyCddddx0lJSXN/h7SfpxOJ6NHj2bFihUR5StWrGD8+PEN6icnJ7NlyxY2b94cfs2YMYPBgwezefNmzj333EY/x+VykZycHPFqS566/6YKQ90BsDkMLE4l1SLSfGrr1dafTNMmPnUib6zdzpVAhc1MqlM95nMFe5qSahFpvjN++e5xz+UN7saSH30nfDz61++Hf9E/1rk5XXnttqMrJp//nyspqfY1qLfn8UubFd/EiRO59957CQQCeDweNm3axAUXXEAwGOSZZ54BYN26dXg8HvLy8hpsmfT0008zefJkHnjgAQAGDRrEmjVreOeddwBwu90kJiZit9sbfdo8ffp0rrnmGgAee+wxnn32WdavX8+UKVOa9T2kfc2aNYsbbriBMWPGMG7cOJ5//nkKCgqYMcOcMvXggw+yf/9+XnrpJaxWK8OHD4+4PiMjg7i4uAbl7cnjC2J1f8MOfznfBayOENQ9UBcRaQ619WrrTybmeqoLDhYBUG4zFyVLqTbLbV2UVItI55OXl0d1dTUbNmxg1apVDBo0iIyMDHJzc9mwYQPV1dXk5+fTu3dv+vXr1+D67du3N9geqTnbJY0YMSL8c0JCAklJSRQVmf8fHjZsGImJiSQmJjJ16tQWfkM5FaZNm8aCBQuYO3cuI0eO5IMPPmD58uX06dMHgMLCwpPuWR1ttYEg9sQvOGL/BACr3QC7O8pRiYi0PbX10RdzPdVGbd3wb7sL8JFUEwLUUy0iLbNt7uTjnrMeswDIxv84/mIcx9Zd/YuGw7NaYsCAAfTs2ZOVK1dSWlpKbm4uAFlZWeTk5PDhhx+ycuVKLrzwwkavNwyjVZ/vcDgiji0WC6GQ+f/d5cuX4/f7AfMpuJxeZs6cycyZMxs99+25dI2ZPXs2s2fPbvugmqHWF8RirQ1vp2V1hCBei5KKSPOprT8xtfUxmFTXL1RWY3cCPhKrzOEZmlMtIi0R72z6/0ZPVd2TqV+UpLS0lPvvvz9cnpuby7vvvsu6dev40Y9+1Oi1Z5xxBuvWrYsoO/bY6XQSDDY+1O1E6ns9RU4Fjz+IxerF7TWPrdmDocfo6AYlIh2S2nq19ScTc0m11W8m1TnWJP598PfoWmsuxKKkWkQ6q7y8PG6//Xb8fn/46TWYDe1Pf/pTamtrG124BOCuu+5i/PjxPPHEE/zgBz/gvffeC8+xqte3b192797N5s2b6dmzJ0lJSbhcrhbHu23bNnw+HyUlJVRWVrJ582aANtsfU2KDLxACWy3xdUm1redQsMbcrDcRiRFq66Mr5loXq9+cRH22M4OHxz5MQrX5xEVbaolIZ5WXl4fH42HAgAERWyLl5uZSWVlJ//79j7tH8NixY3nxxRd59tlnGTlyJO+99x6PPPJIRJ2rrrqKKVOmkJeXR7du3Xj11VdbFe8ll1zCqFGjePvtt8nPz2fUqFGMGjWqVe8pseffv9Ob8wYmHh3+nZAY3YBERE4htfXRFXM91faAmVRbXUkYoRDB0lIAbF01z0pEOqe+ffs2Ol+qZ8+eDconTpzYoOzmm2/m5ptvjii77777wj+7XC7+8pe/NHj/xj7z23tiHs+ePXtOWkekKar91bi95r9Da6KSahHpvNTWR1fMJdXuUA0AVS4X/kO7oW5ugL1LahSjEhERkbZW7a/+Vk+19qgWEZFTI+aGf08fkw7A04GdXP/q5YDZ0FqczmiGJSIiIm3o1fUFpFTfwDCHudWLNVFJtYiInBoxl1TjNRcq8wAJtWaRLSUlevGIiIhIm/tsXzmrtybgrDEfmts0/FtERE6R2Euq67bU8lgMEmrr5lkpqRYREelUav3m9C6n1wNoTrWIiJw6sZdUeysBqDGCJNb3VCcnRzEgERERaWtV3hocXdYQqCkCtPq3iIicOjG3UFl9Uu0xAvTU8G8REZFOqTJQRlzWMgJ1W2dqTrWIiJwqsddTXTf8uybk/9acavVUi4iIdCY1dVtoavVvERE51WIvqa5fqCzkJ9FTN6daw79FREQ6ldpgfVJttvVaqExERE6V2Euq63qqv98zj6GOXgDYUlKjGJCIiIi0tdpADfaAgSNgHmuhMhEROVViK6kOhcJJ9S/Ovpez3AMBLVQmIlIvPz8fi8VCWVnZcessXbqU1NTUk76XxWLhrbfearPYRJrjF5f0DQ/9Bg3/FhGpp7a+7cVWUg0w8yO49R/g7kKwvAzQnGoR6ZwWL15MUlISgUAgXFZVVYXD4WDChAkRdVetWoXFYiE7O5vCwkJSmrGA4+zZsxk5cmRbhc2WLVvIzc3F7XbTo0cP5s6di2EYx61f/8tBY68NGzaE6zV2fvHixW0Wt5xeaoLVuL3mz5b4eCw2W3QDEhE5BdTWnx5tfWyt/m21QsYQAqEAFb4KAuXlgFb/FpHOKS8vj6qqKj7++GPGjh0LmA1qVlYWGzZsoKamhvj4eMBsrLKzsxk0aFA0Q6aiooKLL76YvLw8NmzYwI4dO5g+fToJCQncd999jV4zfvx4CgsLI8r+4z/+g/fff58xY8ZElC9ZsoQpU6aEj5vzC4V0LNW+6nBPtU291CLSSamtPz3a+tjrqQYKKgvIfS2XQwd3AWBN1i9VItL5DB48mOzsbPLz88Nl+fn5XH755fTv3581a9ZElOfl5TU6JGzp0qX07t2b+Ph4rrjiCo4cORJxbs6cOXz66afhJ8JLly4Nny8uLuaKK64gPj6egQMHsmzZshPG/Morr1BbW8vSpUsZPnw4V155JQ899BDz588/7hNsp9NJVlZW+JWWlsayZcu4+eabsVgsEXVTU1Mj6rrd7ibcSeloDMNg9WfZnBWaBmjot4h0XmrrT4+2PiaTak/AA0CittQSkZYyDPBVR+d1guFRx5o4cSIrV64MH69cuZKJEyeSm5sbLvf5fKxdu5a8vLwG13/00UfcfPPNzJw5k82bN5OXl8ejjz4aPj9t2jTuu+8+hg0bRmFhIYWFhUybNi18fs6cOVx99dV89tlnXHLJJVx33XWUlJQcN961a9eSm5uLy+UKl02ePJkDBw6wZ8+eJn3nZcuWUVxczPTp0xucu+OOO0hPT+ecc85h8eLFhEKhJr2ndCz+oMH7n/nZ8UUcoEXKRKSF1NYDauubIraGf9ep8Zsrgjr9ddtsaPifiDSXvwYey47OZz90AJxN63mbOHEi9957L4FAAI/Hw6ZNm7jgggsIBoM888wzAKxbtw6Px0NeXh4FBQUR1z/99NNMnjyZBx54AIBBgwaxZs0a3nnnHQDcbjeJiYnY7XaysrIafP706dO55pprAHjsscd49tlnWb9+fcSwrG87ePAgffv2jSjLzMwMn8vJyTnpd/7973/P5MmT6dWrV0T5r3/9ay666CLcbjf/+Mc/uO+++yguLuaRRx456XtKx+LxBwGID5iTqpVUi0iLqK0H1NY3Rcz2VCfU9VJjsaixFZFOKy8vj+rqajZs2MCqVasYNGgQGRkZ5ObmsmHDBqqrq8nPz6d3797069evwfXbt29n3LhxEWXHHp/IiBEjwj8nJCSQlJREUVERAMOGDSMxMZHExESmTp0arnfsMK76oWDHljdm3759vPvuu9xyyy0Nzj3yyCOMGzeOkSNHct999zF37lyefPLJJn8X6Thq/UFsCV+S7NgJgDVRw79FpPNSW39UtNr62OypDtSEh35bk5OxWGPy2YKItIYj3nyKHK3PbqIBAwbQs2dPVq5cSWlpKbm5uQBkZWWRk5PDhx9+yMqVK7nwwgsbvf5EK3E2KVSHI+LYYrGEh2EtX74cv98PEJ7vlJWVxcGDByOuqW+Y659in8iSJUtIS0vjsssuO2ndsWPHUlFRwaFDh5r03tJxeHxBnF1XkXRgBwC2BD08F5EWUFvftFDV1sdmUu3xe741n1pDv0WkBSyWJg/Lirb6RUlKS0u5//77w+W5ubm8++67rFu3jh/96EeNXnvGGWewbt26iLJjj51OJ8FgsNlx9enTp0HZuHHjeOihh/D5fDidTgDee+89srOzGwwVO5ZhGCxZsoQbb7yxQQPfmE2bNhEXF9ekfTilY/H4g1hsXuK95i+KGpEmIi2itj5Mbf2JxWQXbU2ghgRP3XzqZC1SJiKdW15eHqtXr2bz5s3hp9dgNrQvvPACtbW1jS5cAnDXXXfxzjvv8MQTT7Bjxw5+97vfhedY1evbty+7d+9m8+bNFBcX4/V6Wxzrtddei8vlYvr06WzdupU333yTxx57jFmzZoWHhK1fv54hQ4awf//+iGv/+c9/snv37kaHg7399tu88MILbN26lV27dvHiiy/y8MMP85Of/CRioRTpHDz+IFhrw1tqafVvEens1NZHt62PyaS6T3Ifzk8aCSipFpHOLy8vD4/Hw4ABAyKGPuXm5lJZWUn//v0bLPRRb+zYsbz44os8++yzjBw5kvfee6/BYh9XXXUVU6ZMIS8vj27duvHqq6+2ONaUlBRWrFjBvn37GDNmDDNnzmTWrFnMmjUrXKempoYvv/wyPJys3u9//3vGjx/P0KFDG7yvw+Fg4cKFjBs3jhEjRvD0008zd+5cfvvb37Y4Vjl91fqCWKzeo0m1eqpFpJNTWx/dtt5itGAQ/cKFC3nyyScpLCxk2LBhLFiwgAkTJjRad/Xq1fziF7/giy++oKamhj59+nDbbbdx7733NvnzKioqSElJoby8nOQ2SoJLXnqJQ4/NI/mSqfSYP79N3lNEOqfa2lp2795NTk4OcXFx0Q5H2tGJ/u5PRdsUy9ryfgZDBuNfHcuMP1cy9kuDzF/+B12vvbaNIhWRzkrtfWxqi7a+2XOqX3vtNe655x4WLlzIeeedx3PPPcfUqVPZtm0bvXv3blA/ISGBO+64gxEjRpCQkMDq1au57bbbSEhI4Cc/+UlzP77NBMsrAHOhMhEREelMQtQEanDXjU60qadaREROoWYP/54/fz633HILt956K0OHDmXBggX06tWLRYsWNVp/1KhRXHPNNQwbNoy+ffty/fXXM3nyZFatWtXq4FvKE/DgLzM3JLcla6EyERGRzqQmUAOA26eFykRE5NRrVlLt8/nYuHEjkyZNiiifNGkSa9asadJ7bNq0iTVr1kRMoG9vv/rwV7y9+U+AVv8WERHpbDbsrmBM3H1khtIAsGpLLREROYWaNfy7uLiYYDDYYI+vzMzMBnuNHatnz54cPnyYQCDA7NmzufXWW49b1+v1RqwoV1FR0ZwwT6omUENCeEstDf8WERHpTL46VMvKTd2YWVPXU63Vv0VE5BRq0erf9Uud1zMMo0HZsVatWsXHH3/M4sWLWbBgwQlXjJs3bx4pKSnh1/FWqmupmkANibV1Da3mVIuIiHQqHl8IAKfXA4AtUUm1iIicOs3qqU5PT8dmszXolS4qKmrQe32snJwcAM4880wOHTrE7Nmzueaaaxqt++CDD0YsqV5RUdGmibXH7/lWT3Vqm72viIiIRF9x7UEcSRtx+MzGXnOqRUTkVGpWT7XT6WT06NGsWLEionzFihWMHz++ye9jGMYJNwx3uVwkJydHvNqS2VNt/qzh3yIiIp1LYe0OUrv9GYuhhcpEROTUa/aWWrNmzeKGG25gzJgxjBs3jueff56CggJmzJgBmL3M+/fv56WXXgLgv//7v+nduzdDhgwBzH2rn3rqKe688842/BrNU+OvJtEcEYZNw79FREQ6lRp/FW5f3YHdjsXlimo8IiLSuTU7qZ42bRpHjhxh7ty5FBYWMnz4cJYvX06fPn0AKCwspKCgIFw/FArx4IMPsnv3bux2O/379+fxxx/ntttua7tv0UyBmhrs5nQrrf4tIiLSydQEq4mv36M6IeGk676IiIi0RosWKps5cyZ79uzB6/WyceNGLrjggvC5pUuXkp+fHz6+88472bp1K9XV1ZSXl/PJJ5/w05/+FKu1RR/dJr7b5VzzB7sNS3x81OIQETnd5OfnY7FYKCsrO26dpUuXkpqaetL3slgsvPXWW20Wm0hT1QZrcNcl1Vr5W0Qkktr6the9zDaK7htsDlW3Jafo6bWIdFqLFy8mKSmJQCAQLquqqsLhcDBhwoSIuqtWrcJisZCdnU1hYSEpzRjFM3v2bEaOHNlWYbNlyxZyc3Nxu9306NGDuXPnYtTNjT2evn37YrFYIl4PPPBAm8UkHcv5gxJx+zSfWkQ6P7X1p0db3+zh351BsLwc0NBvEenc8vLyqKqq4uOPP2bs2LGA2aBmZWWxYcMGampqiK8brZOfn092djaDBg2KZshUVFRw8cUXk5eXx4YNG9ixYwfTp08nISGB++6774TXzp07lx//+Mfh40QlUzHLb3jCc6qVVItIZ6a2/vT4f3zM9VSHjBC+slJAi5SJSOc2ePBgsrOzI6bk5Ofnc/nll9O/f3/WrFkTUZ6Xl9fokLClS5fSu3dv4uPjueKKKzhy5EjEuTlz5vDpp5+GnxovXbo0fL64uJgrrriC+Ph4Bg4cyLJly04Y8yuvvEJtbS1Lly5l+PDhXHnllTz00EPMnz//pE+wk5KSyMrKCr9Ol4ZW2l+Vr+ro8G/tUS0inZja+tOjrY+5pHpPxR5+9Y65B7ZV22mJSCvV+GuO+/IGvU2uWxuobVLd5po4cSIrV64MH69cuZKJEyeSm5sbLvf5fKxdu5a8vLwG13/00UfcfPPNzJw5k82bN5OXl8ejjz4aPj9t2jTuu+8+hg0bRmFhIYWFhUybNi18fs6cOVx99dV89tlnXHLJJVx33XWUlJQcN961a9eSm5uL61urNU+ePJkDBw6wZ8+eE37X//zP/yQtLY2RI0fym9/8Bp/Pd8L60nndcMYNXN3zewDYTpNfuESk41Jbr7b+ZGJu+LfH7yEhvEd1alRjEZGO79w/nnvccxN6TGDhdxeGjyf+eSKegKfRumMyx7BkypLw8ZTXp1DqLW1Qb8tNW5oV38SJE7n33nsJBAJ4PB42bdrEBRdcQDAY5JlnngFg3bp1eDwe8vLyInZvAHj66aeZPHlyeM7SoEGDWLNmDe+88w4AbrebxMRE7HY7WVlZDT5/+vTpXHPNNQA89thjPPvss6xfv54pU6Y0Gu/Bgwfp27dvRFlmZmb4XE5OTqPX3X333Zx99tl06dKF9evXh3edePHFF5t4p6QzGZkxkuK4tRxGC5WJSOuprVdbfzIx11NdE6ghodYcVqDh3yLS2eXl5VFdXc2GDRtYtWoVgwYNIiMjg9zcXDZs2EB1dTX5+fn07t2bfv36Nbh++/btjBs3LqLs2OMTGTFiRPjnhIQEkpKSKCoqAmDYsGEkJiaSmJjI1KlTw/WOXUCyfijYiRaWvPfee8nNzWXEiBHceuutLF68mN///vcRw9cktgSrqgCwJqinWkQ6N7X10W/rY6+nOuAhse7hkU3Dv0WklT669qPjnrNZbRHH+VfnH7eu1RL5jPOdq95pVVz1BgwYQM+ePVm5ciWlpaXk5uYCkJWVRU5ODh9++CErV67kwgsvbPT6k81tOhmHwxFxbLFYCIVCACxfvhy/3w+YT8Hr4zp48GDENfUNc/1T7KaoX6zlq6++Ii0trWXBS4cWqqoGtFCZiLSe2voTU1sfg0l1TaCGxPDwb63+LSKtE+9o+l73p6ruydQvSlJaWsr9998fLs/NzeXdd99l3bp1/OhHP2r02jPOOIN169ZFlB177HQ6CQaDzY6rT58+DcrGjRvHQw89hM/nw+l0AvDee++RnZ3dYKjYiWzatAmA7t27Nzsu6RxC1fVJtYZ/i0jrqK1XW38yMTf8+9tzqq3JSqpFpPPLy8tj9erVbN68Ofz0GsyG9oUXXqC2trbRhUsA7rrrLt555x2eeOIJduzYwe9+97vwHKt6ffv2Zffu3WzevJni4mK8Xm+j79UU1157LS6Xi+nTp7N161befPNNHnvsMWbNmhUeErZ+/XqGDBnC/v37AXPBk//6r/9i8+bN7N69mz//+c/cdtttXHbZZfTu3bvFsUjHFqob/q2FykQkFqitj66YS6rNnuq6OdUa/i0iMSAvLw+Px8OAAQMihlXl5uZSWVlJ//796dWrV6PXjh07lhdffJFnn32WkSNH8t577/HII49E1LnqqquYMmUKeXl5dOvWjVdffbXFsaakpLBixQr27dvHmDFjmDlzJrNmzWLWrFnhOjU1NXz55Zfh4WQul4vXXnuNiRMncsYZZ/DLX/6SH//4x62KQzq++qRaw79FJBaorY8ui9HaQfTtoKKigpSUFMrLy0lu5eJi/yz4J+7rfkbqYQ99XnmZ+NGj2yhKEemsamtr2b17Nzk5OcTFxUU7HGlHJ/q7b8u2Sdr+fn59xZV4t2+n1wvPkzhhQhtEKCKdndr72NQWbX3M9VRf2PtC0vzmnmha/VtERKRzCmn1bxERaScxl1QboRDBigpAc6pFREQ6q6PDv7VQmYiInFoxl1QHq6qgbol3zakWERHpnOpX/9ZCZSIicqrFXFL9mxUPAhBy2rFqroSIiEinE/L5MHw+QAuViYjIqRdzSbVRUQlAMNEd5UhEREROXwsXLgwv2jJ69GhWrVp13LqrV6/mvPPOIy0tDbfbzZAhQ/iv//qvdow2Un0vNYA1QcO/RUTk1LJHO4B2V2k2tEaSGlkREZHGvPbaa9xzzz0sXLiQ8847j+eee46pU6eybdu2RvcDTUhI4I477mDEiBEkJCSwevVqbrvtNhISEvjJT37S7vHXz6e2uN1YbLZ2/3wREYktMddTba2sMX9I0nAwERGRxsyfP59bbrmFW2+9laFDh7JgwQJ69erFokWLGq0/atQorrnmGoYNG0bfvn25/vrrmTx58gl7t08lLVImIiLtKeaSaluVmVRbk5OiHImIiMjpx+fzsXHjRiZNmhRRPmnSJNasWdOk99i0aRNr1qwhNzf3VIR4UvVJtU3baYmISDuIueHf9movALYUbaclIiJyrOLiYoLBIJmZmRHlmZmZHDx48ITX9uzZk8OHDxMIBJg9eza33nrrcet6vV68Xm/4uKJuu8u2EAz3VCupFhGRUy/meqoddUm1XUm1iEgD+fn5WCwWysrKjltn6dKlpKamnvS9LBYLb731VpvFJu3LYrFEHBuG0aDsWKtWreLjjz9m8eLFLFiwgFdfffW4defNm0dKSkr41atXrzaJGyBUXTcqTUm1iEgDauvbXswl1X0s6QC4UtOiHImIyKm1ePFikpKSCAQC4bKqqiocDgcTJkyIqLtq1SosFgvZ2dkUFhaS0owHj7Nnz2bkyJFtFTZbtmwhNzcXt9tNjx49mDt3LoZhHLf+nj17uOWWW8jJycHtdtO/f39+9atf4avbUqmexWJp8Fq8eHGbxd1ZpKenY7PZGvRKFxUVNei9PlZOTg5nnnkmP/7xj7n33nuZPXv2ces++OCDlJeXh1979+5ti/ABzakWkdihtv70aOtjbvj3uQnDqGQfSenZ0Q5FROSUysvLo6qqio8//pixY8cCZoOalZXFhg0bqKmpIT4+HjCfWmdnZzNo0KBohkxFRQUXX3wxeXl5bNiwgR07djB9+nQSEhK47777Gr3miy++IBQK8dxzzzFgwAC2bt3Kj3/8Y6qrq3nqqaci6i5ZsoQpU6aEj5vzC0WscDqdjB49mhUrVnDFFVeEy1esWMHll1/e5PcxDCNiePexXC4XLperVbEeT6i6fk61kmoR6dzU1p8ebX3M9VQHK8oBsKUkRzkSEZFTa/DgwWRnZ5Ofnx8uy8/P5/LLL6d///4Ri07l5+eTl5fX6JCwpUuX0rt3b+Lj47niiis4cuRIxLk5c+bw6aefhp8IL126NHy+uLiYK664gvj4eAYOHMiyZctOGPMrr7xCbW0tS5cuZfjw4Vx55ZU89NBDzJ8//7hPsKdMmcKSJUuYNGkS/fr147LLLuNnP/sZb7zxRoO6qampZGVlhV9ut/skdzE2zZo1ixdffJE//OEPbN++nXvvvZeCggJmzJgBmL3MN954Y7j+f//3f/P222+zc+dOdu7cyZIlS3jqqae4/vrroxJ/eE61FioTkU5Obf3p0dbHXFIdKjcXQrElK6kWkZYzDINQTU1UXicaHnWsiRMnsnLlyvDxypUrmThxIrm5ueFyn8/H2rVrycvLa3D9Rx99xM0338zMmTPZvHkzeXl5PProo+Hz06ZN47777mPYsGEUFhZSWFjItGnTwufnzJnD1VdfzWeffcYll1zCddddR0lJyXHjXbt2Lbm5uRE9mJMnT+bAgQPs2bOnyd+7vLycrl27Nii/4447SE9P55xzzmHx4sWEQqEmv2csmTZtGgsWLGDu3LmMHDmSDz74gOXLl9OnTx8ACgsLKSgoCNcPhUI8+OCDjBw5kjFjxvDss8/y+OOPM3fu3KjEH6qqBjSnWkRaR229SW39ycXc8O9geX1PtYb8iUjLGR4PX549OiqfPfiTjVjqhnKdzMSJE7n33nsJBAJ4PB42bdrEBRdcQDAY5JlnngFg3bp1eDwe8vLyIhIlgKeffprJkyfzwAMPADBo0CDWrFnDO++8A4Db7SYxMRG73U5WVlaDz58+fTrXXHMNAI899hjPPvss69evjxiW9W0HDx6kb9++EWX183gPHjxITk7OSb/zrl27ePbZZ/ntb38bUf7rX/+aiy66CLfbzT/+8Q/uu+8+iouLeeSRR076nrFo5syZzJw5s9Fz3+6hALjzzju588472yGqpglp9W8RaQNq69XWN1XM9VQH67bssCYrqRaRzi8vL4/q6mo2bNjAqlWrGDRoEBkZGeTm5rJhwwaqq6vJz8+nd+/e9OvXr8H127dvZ9y4cRFlxx6fyIgRI8I/JyQkkJSURFFREQDDhg0jMTGRxMREpk6dGq7X2KrTjZU35sCBA0yZMoUf/vCHDbZzeuSRRxg3bhwjR47kvvvuY+7cuTz55JNN/i7ScYSq63uqNadaRDo/tfVHRautj6meaiMYJFRZCWhOtYi0jsXtZvAnG6P22U01YMAAevbsycqVKyktLSU3NxeArKwscnJy+PDDD1m5ciUXXnhho9c3Z/hZYxwOR8SxxWIJD8Navnw5fr8fIDzfKSsrq9FVp4GTrjx94MAB8vLyGDduHM8///xJYxs7diwVFRUcOnTopO8tHUt9T7VNPdUi0gpq65tGbX2MJdVYLOS88TrB8nIN/xaRVrFYLE0elhVt9YuSlJaWcv/994fLc3Nzeffdd1m3bh0/+tGPGr32jDPOYN26dRFlxx47nU6CwWCz46qfn/tt48aN46GHHsLn8+F0OgF47733yM7ObjBU7Nv2799PXl4eo0ePZsmSJVitJx+ItWnTJuLi4pq0D6d0LJn/8QjBI0dwNvJvTESkqdTWH6W2/sRiavi3xWol7owzSBg3Dos9tp4niEjsysvLY/Xq1WzevDn89BrMhvaFF16gtra20YVLAO666y7eeecdnnjiCXbs2MHvfve78Byren379mX37t1s3ryZ4uLiE26jdDLXXnstLpeL6dOns3XrVt58800ee+wxZs2aFR4Stn79eoYMGcL+/fsB86n1xIkT6dWrF0899RSHDx/m4MGDEU/B3377bV544QW2bt3Krl27ePHFF3n44Yf5yU9+csq2dZLoceXkED9mDPZu3aIdiohIu1BbH+W23ugAysvLDcAoLy+PdigiEoM8Ho+xbds2w+PxRDuUFtm9e7cBGEOGDIko37t3rwEY/fv3D5etXLnSAIzS0tJw2e9//3ujZ8+ehtvtNr7//e8bTz31lJGSkhI+X1tba1x11VVGamqqARhLliwxDMMwAOPNN9+M+MyUlJTw+eP57LPPjAkTJhgul8vIysoyZs+ebYRCoQYx7t692zAMw1iyZIkBNPqq93//93/GyJEjjcTERCM+Pt4YPny4sWDBAsPv958wlhP93attalu6nyISbR25vVdbH9223mIYrRxE3w4qKipISUmhvLycZG2FJSLtrLa2lt27d5OTk0NcXFy0w5F2dKK/e7VNbUv3U0SiTe19bGqLtj6mhn+LiIiIiIiItCUl1SIiIiIiIiItpKRaREREREREpIWUVIuIiIiIiIi0kJJqERERERERkRZSUi0i0kShUCjaIUg709+5iEjs6QCbI0kbaou23t4GcYiIdGpOpxOr1cqBAwfo1q0bTqcTi8US7bDkFDIMA5/Px+HDh7FarTidzmiHJCIip5jD4cBisXD48GG6deumtr6Ta8u2Xkm1iMhJWK1WcnJyKCws5MCBA9EOR9pRfHw8vXv3xmrVwC4Rkc7OZrPRs2dP9u3bx549e6IdjrSTtmjrlVSLiDSB0+mkd+/eBAIBgsFgtMORdmCz2bDb7eqpEBGJIYmJiQwcOBC/3x/tUKQdtFVb36KkeuHChTz55JMUFhYybNgwFixYwIQJExqt+8Ybb7Bo0SI2b96M1+tl2LBhzJ49m8mTJ7cqcBGR9maxWHA4HDgcjmiHIiIiIqeIzWbDZrNFOwzpQJrdx/3aa69xzz338PDDD7Np0yYmTJjA1KlTKSgoaLT+Bx98wMUXX8zy5cvZuHEjeXl5fP/732fTpk2tDl5EREREREQkmixGM5e3O/fcczn77LNZtGhRuGzo0KH84Ac/YN68eU16j2HDhjFt2jR++ctfNql+RUUFKSkplJeXk5yc3JxwRURETgm1TW1L91NERE43TW2bmtVT7fP52LhxI5MmTYoonzRpEmvWrGnSe4RCISorK+natWtzPlpERERERETktNOsOdXFxcUEg0EyMzMjyjMzMzl48GCT3uO3v/0t1dXVXH311cet4/V68Xq94ePy8nLAfFIgIiJyOqhvk7Sfaduov49q60VE5HTR1La+RQuVHbs6mmEYTVox7dVXX2X27Nn89a9/JSMj47j15s2bx5w5cxqU9+rVq/nBioiInEKVlZWkpKREO4wOr7KyElBbLyIip5+TtfXNSqrT09Ox2WwNeqWLiooa9F4f67XXXuOWW27hf//3f/nud797wroPPvggs2bNCh+HQiFKSkpIS0tr9XLnFRUV9OrVi71792rOVjPovjWf7lnL6L61jO5b87X2nhmGQWVlJdnZ2acgutiTnZ3N3r17SUpKUlsfJbpvLaP71ny6Zy2j+9YyrblvTW3rm5VUO51ORo8ezYoVK7jiiivC5StWrODyyy8/7nWvvvoqN998M6+++iqXXnrpST/H5XLhcrkiylJTU5sT6kklJyfrH2ML6L41n+5Zy+i+tYzuW/O15p6ph7rtWK1Wevbs2abvqf8eWkb3rWV035pP96xldN9apqX3rSltfbOHf8+aNYsbbriBMWPGMG7cOJ5//nkKCgqYMWMGYPYy79+/n5deegkwE+obb7yRp59+mrFjx4Z7ud1ut34ZERERERERkQ6t2Un1tGnTOHLkCHPnzqWwsJDhw4ezfPly+vTpA0BhYWHEntXPPfccgUCA22+/ndtvvz1cftNNN7F06dLWfwMRERERERGRKGnRQmUzZ85k5syZjZ47NlHOz89vyUecMi6Xi1/96lcNhpfLiem+NZ/uWcvovrWM7lvz6Z51Xvq7bRndt5bRfWs+3bOW0X1rmfa4bxZDe4GIiIiIiIiItIg12gGIiIiIiIiIdFRKqkVERERERERaSEm1iIiIiIiISAspqRYRERERERFpoZhKqhcuXEhOTg5xcXGMHj2aVatWRTuk08q8efM455xzSEpKIiMjgx/84Ad8+eWXEXUMw2D27NlkZ2fjdruZOHEin3/+eZQiPv3MmzcPi8XCPffcEy7TPWvc/v37uf7660lLSyM+Pp6RI0eycePG8Hndt4YCgQCPPPIIOTk5uN1u+vXrx9y5cwmFQuE6sX7fPvjgA77//e+TnZ2NxWLhrbfeijjflPvj9Xq58847SU9PJyEhgcsuu4x9+/a147eQ1lJ7f3xq61tPbX3Tqa1vPrX1TXPatfdGjPjTn/5kOBwO44UXXjC2bdtm3H333UZCQoLxzTffRDu008bkyZONJUuWGFu3bjU2b95sXHrppUbv3r2NqqqqcJ3HH3/cSEpKMl5//XVjy5YtxrRp04zu3bsbFRUVUYz89LB+/Xqjb9++xogRI4y77747XK571lBJSYnRp08fY/r06cZHH31k7N6923j//feNr776KlxH962hRx991EhLSzP+9re/Gbt37zb+93//10hMTDQWLFgQrhPr92358uXGww8/bLz++usGYLz55psR55tyf2bMmGH06NHDWLFihfHJJ58YeXl5xllnnWUEAoF2/jbSEmrvT0xtfeuorW86tfUto7a+aU639j5mkurvfOc7xowZMyLKhgwZYjzwwANRiuj0V1RUZADGv/71L8MwDCMUChlZWVnG448/Hq5TW1trpKSkGIsXL45WmKeFyspKY+DAgcaKFSuM3NzccEOre9a4X/ziF8b5559/3PO6b4279NJLjZtvvjmi7MorrzSuv/56wzB03451bCPblPtTVlZmOBwO409/+lO4zv79+w2r1Wq888477Ra7tJza++ZRW990auubR219y6itb77Tob2PieHfPp+PjRs3MmnSpIjySZMmsWbNmihFdforLy8HoGvXrgDs3r2bgwcPRtxHl8tFbm5uzN/H22+/nUsvvZTvfve7EeW6Z41btmwZY8aM4Yc//CEZGRmMGjWKF154IXxe961x559/Pv/4xz/YsWMHAJ9++imrV6/mkksuAXTfTqYp92fjxo34/f6IOtnZ2QwfPlz3sANQe998auubTm1986itbxm19a0Xjfbe3vqwT3/FxcUEg0EyMzMjyjMzMzl48GCUojq9GYbBrFmzOP/88xk+fDhA+F41dh+/+eabdo/xdPGnP/2JTz75hA0bNjQ4p3vWuK+//ppFixYxa9YsHnroIdavX89dd92Fy+Xixhtv1H07jl/84heUl5czZMgQbDYbwWCQ3/zmN1xzzTWA/r2dTFPuz8GDB3E6nXTp0qVBHbUXpz+1982jtr7p1NY3n9r6llFb33rRaO9jIqmuZ7FYIo4Nw2hQJqY77riDzz77jNWrVzc4p/t41N69e7n77rt57733iIuLO2493bNIoVCIMWPG8NhjjwEwatQoPv/8cxYtWsSNN94Yrqf7Fum1117j5Zdf5o9//CPDhg1j8+bN3HPPPWRnZ3PTTTeF6+m+nVhL7o/uYcei/waaRm1906itbxm19S2jtr7ttGd7HxPDv9PT07HZbA2eOhQVFTV4giFw5513smzZMlauXEnPnj3D5VlZWQC6j9+yceNGioqKGD16NHa7Hbvdzr/+9S+eeeYZ7HZ7+L7onkXq3r07Z5xxRkTZ0KFDKSgoAPRv7Xjuv/9+HnjgAf793/+dM888kxtuuIF7772XefPmAbpvJ9OU+5OVlYXP56O0tPS4deT0pfa+6dTWN53a+pZRW98yautbLxrtfUwk1U6nk9GjR7NixYqI8hUrVjB+/PgoRXX6MQyDO+64gzfeeIN//vOf5OTkRJzPyckhKysr4j76fD7+9a9/xex9vOiii9iyZQubN28Ov8aMGcN1113H5s2b6devn+5ZI84777wGW7js2LGDPn36APq3djw1NTVYrZH/27bZbOFtNnTfTqwp92f06NE4HI6IOoWFhWzdulX3sANQe39yauubT219y6itbxm19a0Xlfa+2UubdVD1W2z8/ve/N7Zt22bcc889RkJCgrFnz55oh3ba+OlPf2qkpKQY+fn5RmFhYfhVU1MTrvP4448bKSkpxhtvvGFs2bLFuOaaa2JuCf+T+faKoIahe9aY9evXG3a73fjNb35j7Ny503jllVeM+Ph44+WXXw7X0X1r6KabbjJ69OgR3mbjjTfeMNLT042f//zn4Tqxft8qKyuNTZs2GZs2bTIAY/78+camTZvC2yk15f7MmDHD6Nmzp/H+++8bn3zyiXHhhRdqS60ORO39iamtbxtq609ObX3LqK1vmtOtvY+ZpNowDOO///u/jT59+hhOp9M4++yzw9tHiAlo9LVkyZJwnVAoZPzqV78ysrKyDJfLZVxwwQXGli1bohf0aejYhlb3rHFvv/22MXz4cMPlchlDhgwxnn/++Yjzum8NVVRUGHfffbfRu3dvIy4uzujXr5/x8MMPG16vN1wn1u/bypUrG/3/2E033WQYRtPuj8fjMe644w6ja9euhtvtNr73ve8ZBQUFUfg20lJq749PbX3bUFvfNGrrm09tfdOcbu29xTAMo/n92yIiIiIiIiISE3OqRURERERERE4FJdUiIiIiIiIiLaSkWkRERERERKSFlFSLiIiIiIiItJCSahEREREREZEWUlItIiIiIiIi0kJKqkVERERERERaSEm1iIiIiIiISAspqRYRERERERFpISXVIiIiIiIiIi2kpFpERERERESkhZRUi4iIiIiIiLTQ/we14pHDSzNXOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = width_multiplier_1.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(width_multiplier_1[field], label=\"Width-1\", linestyle=\"--\")\n",
    "    plt.plot(width_multiplier_75[field], label=\"Width-0.75\")\n",
    "    plt.plot(width_multiplier_50[field], label=\"Width-0.5\", linestyle=\"--\")\n",
    "    plt.plot(width_multiplier_25[field], label=\"Width-0.25\", linestyle=\"-\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18c2f2e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
